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models</title> <meta name="description" content="Search results for: transformer models"> <meta name="keywords" content="transformer models"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="transformer models"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6902</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: transformer models</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6902</span> A Novel Approach of Power Transformer Diagnostic Using 3D FEM Parametrical Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Brandt">M. Brandt</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Peniak"> A. Peniak</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Makarovi%C4%8D"> J. Makarovič</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Rafajdus"> P. Rafajdus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with a novel approach of power transformers diagnostics. This approach identifies the exact location and the range of a fault in the transformer and helps to reduce operation costs related to handling of the faulty transformer, its disassembly and repair. The advantage of the approach is a possibility to simulate healthy transformer and also all faults, which can occur in transformer during its operation without its disassembling, which is very expensive in practice. The approach is based on creating frequency dependent impedance of the transformer by sweep frequency response analysis measurements and by 3D FE parametrical modeling of the fault in the transformer. The parameters of the 3D FE model are the position and the range of the axial short circuit. Then, by comparing the frequency dependent impedances of the parametrical models with the measured ones, the location and the range of the fault is identified. The approach was tested on a real transformer and showed high coincidence between the real fault and the simulated one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transformer" title="transformer">transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=parametrical%20model%20of%20transformer" title=" parametrical model of transformer"> parametrical model of transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=fault" title=" fault"> fault</a>, <a href="https://publications.waset.org/abstracts/search?q=sweep%20frequency%20response%20analysis" title=" sweep frequency response analysis"> sweep frequency response analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a> </p> <a href="https://publications.waset.org/abstracts/13144/a-novel-approach-of-power-transformer-diagnostic-using-3d-fem-parametrical-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6901</span> Heat Distribution Simulation on Transformer Using FEMM Software</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20K.%20Mohd%20Affendi">N. K. Mohd Affendi</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20A.%20R.%20Tuan%20Abdullah"> T. A. R. Tuan Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Syed%20Mustaffa"> S. A. Syed Mustaffa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In power industry transformer is an important component and most of us familiar by the functioning principle of a transformer electrically. There are many losses occur during the operation of a transformer that causes heat generation. This heat, if not dissipated properly will reduce the lifetime and effectiveness of the transformer. Transformer cooling helps in maintaining the temperature rise of various paths. This paper proposed to minimize the ambient temperature of the transformer room in order to lower down the temperature of the transformer. A simulation has been made using finite element methods programs called FEMM (Finite Elements Method Magnetics) to create a virtual model based on actual measurement of a transformer. The generalization of the two-dimensional (2D) FEMM results proves that by minimizing the ambient temperature, the heat of the transformer is decreased. The modeling process and of the transformer heat flow has been presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20generation" title="heat generation">heat generation</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20rise" title=" temperature rise"> temperature rise</a>, <a href="https://publications.waset.org/abstracts/search?q=ambient%20temperature" title=" ambient temperature"> ambient temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=FEMM" title=" FEMM"> FEMM</a> </p> <a href="https://publications.waset.org/abstracts/4755/heat-distribution-simulation-on-transformer-using-femm-software" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4755.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6900</span> Neural Machine Translation for Low-Resource African Languages: Benchmarking State-of-the-Art Transformer for Wolof</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheikh%20Bamba%20Dione">Cheikh Bamba Dione</a>, <a href="https://publications.waset.org/abstracts/search?q=Alla%20Lo"> Alla Lo</a>, <a href="https://publications.waset.org/abstracts/search?q=Elhadji%20Mamadou%20Nguer"> Elhadji Mamadou Nguer</a>, <a href="https://publications.waset.org/abstracts/search?q=Siley%20O.%20Ba"> Siley O. Ba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose two neural machine translation (NMT) systems (French-to-Wolof and Wolof-to-French) based on sequence-to-sequence with attention and transformer architectures. We trained our models on a parallel French-Wolof corpus of about 83k sentence pairs. Because of the low-resource setting, we experimented with advanced methods for handling data sparsity, including subword segmentation, back translation, and the copied corpus method. We evaluate the models using the BLEU score and find that transformer outperforms the classic seq2seq model in all settings, in addition to being less sensitive to noise. In general, the best scores are achieved when training the models on word-level-based units. For subword-level models, using back translation proves to be slightly beneficial in low-resource (WO) to high-resource (FR) language translation for the transformer (but not for the seq2seq) models. A slight improvement can also be observed when injecting copied monolingual text in the target language. Moreover, combining the copied method data with back translation leads to a substantial improvement of the translation quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=backtranslation" title="backtranslation">backtranslation</a>, <a href="https://publications.waset.org/abstracts/search?q=low-resource%20language" title=" low-resource language"> low-resource language</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20machine%20translation" title=" neural machine translation"> neural machine translation</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence-to-sequence" title=" sequence-to-sequence"> sequence-to-sequence</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=Wolof" title=" Wolof"> Wolof</a> </p> <a href="https://publications.waset.org/abstracts/135110/neural-machine-translation-for-low-resource-african-languages-benchmarking-state-of-the-art-transformer-for-wolof" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135110.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6899</span> Power Transformer Risk-Based Maintenance by Optimization of Transformer Condition and Transformer Importance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kitti%20Leangkrua">Kitti Leangkrua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a risk-based maintenance strategy of a power transformer in order to optimize operating and maintenance costs. The methodology involves the study and preparation of a database for the collection the technical data and test data of a power transformer. An evaluation of the overall condition of each transformer is performed by a program developed as a result of the measured results; in addition, the calculation of the main equipment separation to the overall condition of the transformer (% HI) and the criteria for evaluating the importance (% ImI) of each location where the transformer is installed. The condition assessment is performed by analysis test data such as electrical test, insulating oil test and visual inspection. The condition of the power transformer will be classified from very poor to very good condition. The importance is evaluated from load criticality, importance of load and failure consequence. The risk matrix is developed for evaluating the risk of each power transformer. The high risk power transformer will be focused firstly. The computerized program is developed for practical use, and the maintenance strategy of a power transformer can be effectively managed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asset%20management" title="asset management">asset management</a>, <a href="https://publications.waset.org/abstracts/search?q=risk-based%20maintenance" title=" risk-based maintenance"> risk-based maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20transformer" title=" power transformer"> power transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20index" title=" health index"> health index</a> </p> <a href="https://publications.waset.org/abstracts/78262/power-transformer-risk-based-maintenance-by-optimization-of-transformer-condition-and-transformer-importance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78262.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6898</span> A Review of Transformer Modeling for Power Line Communication Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Balarabe%20Nkom">Balarabe Nkom</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20P.%20R.%20Taylor"> Adam P. R. Taylor</a>, <a href="https://publications.waset.org/abstracts/search?q=Craig%20Baguley"> Craig Baguley</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Power Line Communications (PLC) is being employed in existing power systems, despite the infrastructure not being designed with PLC considerations in mind. Given that power transformers can last for decades, the distribution transformer in particular exists as a relic of un-optimized technology. To determine issues that may need to be addressed in subsequent designs of such transformers, it is essential to have a highly accurate transformer model for simulations and subsequent optimization for the PLC environment, with a view to increase data speed, throughput, and efficiency, while improving overall system stability and reliability. This paper reviews various methods currently available for creating transformer models and provides insights into the requirements of each for obtaining high accuracy. The review indicates that a combination of traditional analytical methods using a hybrid approach gives good accuracy at reasonable costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distribution%20transformer" title="distribution transformer">distribution transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20line%20communications" title=" power line communications"> power line communications</a> </p> <a href="https://publications.waset.org/abstracts/7143/a-review-of-transformer-modeling-for-power-line-communication-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7143.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">508</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6897</span> Experimental Partial Discharge Localization for Internal Short Circuits of Transformers Windings </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalal%20M.%20Abdallah">Jalal M. Abdallah </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents experimental studies carried out on a three phase transformer to investigate and develop the transformer models, which help in testing procedures, describing and evaluating the transformer dielectric conditions process and methods such as: the partial discharge (PD) localization in windings. The measurements are based on the transfer function methods in transformer windings by frequency response analysis (FRA). Numbers of tests conditions were applied to obtain the sensitivity frequency responses of a transformer for different type of faults simulated in a particular phase. The frequency responses were analyzed for the sensitivity of different test conditions to detect and identify the starting of small faults, which are sources of PD. In more detail, the aim is to explain applicability and sensitivity of advanced PD measurements for small short circuits and its localization. The experimental results presented in the paper will help in understanding the sensitivity of FRA measurements in detecting various types of internal winding short circuits in the transformer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20response%20analysis%20%28FRA%29" title="frequency response analysis (FRA)">frequency response analysis (FRA)</a>, <a href="https://publications.waset.org/abstracts/search?q=measurements" title=" measurements"> measurements</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20function" title=" transfer function"> transfer function</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/7119/experimental-partial-discharge-localization-for-internal-short-circuits-of-transformers-windings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7119.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6896</span> Improvement of the 3D Finite Element Analysis of High Voltage Power Transformer Defects in Time Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Rashid%20Hussain">M. Rashid Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shady%20S.%20Refaat"> Shady S. Refaat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The high voltage power transformer is the most essential part of the electrical power utilities. Reliability on the transformers is the utmost concern, and any failure of the transformers can lead to catastrophic losses in electric power utility. The causes of transformer failure include insulation failure by partial discharge, core and tank failure, cooling unit failure, current transformer failure, etc. For the study of power transformer defects, finite element analysis (FEA) can provide valuable information on the severity of defects. FEA provides a more accurate representation of complex geometries because they consider thermal, electrical, and environmental influences on the insulation models to obtain basic characteristics of the insulation system during normal and partial discharge conditions. The purpose of this paper is the time domain analysis of defects 3D model of high voltage power transformer using FEA to study the electric field distribution at different points on the defects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20transformer" title="power transformer">power transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=dielectric%20response" title=" dielectric response"> dielectric response</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20discharge" title=" partial discharge"> partial discharge</a>, <a href="https://publications.waset.org/abstracts/search?q=insulation" title=" insulation"> insulation</a> </p> <a href="https://publications.waset.org/abstracts/113040/improvement-of-the-3d-finite-element-analysis-of-high-voltage-power-transformer-defects-in-time-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113040.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6895</span> Ultraviolet Visible Spectroscopy Analysis on Transformer Oil by Correlating It with Various Oil Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajnish%20Shrivastava">Rajnish Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20R.%20Sood"> Y. R. Sood</a>, <a href="https://publications.waset.org/abstracts/search?q=Priti%20Pundir"> Priti Pundir</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Srivastava"> Rahul Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Power transformer is one of the most important devices that are used in power station. Due to several fault impending upon it or due to ageing, etc its life gets lowered. So, it becomes necessary to have diagnosis of oil for fault analysis. Due to the chemical, electrical, thermal and mechanical stress the insulating material in the power transformer degraded. It is important to regularly assess the condition of oil and the remaining life of the power transformer. In this paper UV-VIS absorption graph area is correlated with moisture content, Flash point, IFT and Density of Transformer oil. Since UV-VIS absorption graph area varies accordingly with the variation in different transformer parameters. So by obtaining the correlation among different oil parameters for oil with respect to UV-VIS absorption area, decay contents of transformer oil can be predicted <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breakdown%20voltage%20%28BDV%29" title="breakdown voltage (BDV)">breakdown voltage (BDV)</a>, <a href="https://publications.waset.org/abstracts/search?q=interfacial%20Tension%20%28IFT%29" title=" interfacial Tension (IFT)"> interfacial Tension (IFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=moisture%20content" title=" moisture content"> moisture content</a>, <a href="https://publications.waset.org/abstracts/search?q=ultra%20violet-visible%20rays%20spectroscopy%20%28UV-VIS%29" title=" ultra violet-visible rays spectroscopy (UV-VIS)"> ultra violet-visible rays spectroscopy (UV-VIS)</a> </p> <a href="https://publications.waset.org/abstracts/27975/ultraviolet-visible-spectroscopy-analysis-on-transformer-oil-by-correlating-it-with-various-oil-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27975.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">642</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6894</span> ANFIS Based Technique to Estimate Remnant Life of Power Transformer by Predicting Furan Contents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priyesh%20Kumar%20Pandey">Priyesh Kumar Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Zakir%20Husain"> Zakir Husain</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20K.%20Jarial"> R. K. Jarial</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Condition monitoring and diagnostic is important for testing of power transformer in order to estimate the remnant life. Concentration of furan content in transformer oil can be a promising indirect measurement of the aging of transformer insulation. The oil gets contaminated mainly due to ageing. The present paper introduces adaptive neuro fuzzy technique to correlate furanic compounds obtained by high performance liquid chromatography (HPLC) test and remnant life of the power transformer. The results are obtained by conducting HPLC test at TIFAC-CORE lab, NIT Hamirpur on thirteen power transformer oil samples taken from Himachal State Electricity Board, India. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20technique" title="adaptive neuro fuzzy technique">adaptive neuro fuzzy technique</a>, <a href="https://publications.waset.org/abstracts/search?q=furan%20compounds" title=" furan compounds"> furan compounds</a>, <a href="https://publications.waset.org/abstracts/search?q=remnant%20life" title=" remnant life"> remnant life</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20oil" title=" transformer oil"> transformer oil</a> </p> <a href="https://publications.waset.org/abstracts/11916/anfis-based-technique-to-estimate-remnant-life-of-power-transformer-by-predicting-furan-contents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6893</span> Research on Placement Method of the Magnetic Flux Leakage Sensor Based on Online Detection of the Transformer Winding Deformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zheng">Wei Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Mao%20Ji"> Mao Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhe%20Hou"> Zhe Hou</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng%20Huang"> Meng Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Qi"> Bo Qi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The transformer is the key equipment of the power system. Winding deformation is one of the main transformer defects, and timely and effective detection of the transformer winding deformation can ensure the safe and stable operation of the transformer to the maximum extent. When winding deformation occurs, the size, shape and spatial position of the winding will change, which directly leads to the change of magnetic flux leakage distribution. Therefore, it is promising to study the online detection method of the transformer winding deformation based on magnetic flux leakage characteristics, in which the key step is to study the optimal placement method of magnetic flux leakage sensors inside the transformer. In this paper, a simulation model of the transformer winding deformation is established to obtain the internal magnetic flux leakage distribution of the transformer under normal operation and different winding deformation conditions, and the law of change of magnetic flux leakage distribution due to winding deformation is analyzed. The results show that different winding deformation leads to different characteristics of the magnetic flux leakage distribution. On this basis, an optimized placement of magnetic flux leakage sensors inside the transformer is proposed to provide a basis for the online detection method of transformer winding deformation based on the magnetic flux leakage characteristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20flux%20leakage" title="magnetic flux leakage">magnetic flux leakage</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20placement%20method" title=" sensor placement method"> sensor placement method</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=winding%20deformation" title=" winding deformation"> winding deformation</a> </p> <a href="https://publications.waset.org/abstracts/136348/research-on-placement-method-of-the-magnetic-flux-leakage-sensor-based-on-online-detection-of-the-transformer-winding-deformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136348.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6892</span> JaCoText: A Pretrained Model for Java Code-Text Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jessica%20Lopez%20Espejel">Jessica Lopez Espejel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahaman%20Sanoussi%20Yahaya%20Alassan"> Mahaman Sanoussi Yahaya Alassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Dahhane"> Walid Dahhane</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Hassane%20Ettifouri"> El Hassane Ettifouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pretrained transformer-based models have shown high performance in natural language generation tasks. However, a new wave of interest has surged: automatic programming language code generation. This task consists of translating natural language instructions to a source code. Despite the fact that well-known pre-trained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformer neural network. It aims to generate java source code from natural language text. JaCoText leverages the advantages of both natural language and code generation models. More specifically, we study some findings from state of the art and use them to (1) initialize our model from powerful pre-trained models, (2) explore additional pretraining on our java dataset, (3) lead experiments combining the unimodal and bimodal data in training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=java%20code%20generation" title="java code generation">java code generation</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence-to-sequence%20models" title=" sequence-to-sequence models"> sequence-to-sequence models</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20neural%20networks" title=" transformer neural networks"> transformer neural networks</a> </p> <a href="https://publications.waset.org/abstracts/156766/jacotext-a-pretrained-model-for-java-code-text-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156766.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6891</span> Diagnostic of Breakdown in High Voltage Bushing Power Transformer 500 kV Cirata Substation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andika%20Bagaskara">Andika Bagaskara</a>, <a href="https://publications.waset.org/abstracts/search?q=Andhika%20Rizki%20Pratama"> Andhika Rizki Pratama</a>, <a href="https://publications.waset.org/abstracts/search?q=Lalu%20Arya%20Repatmaja"> Lalu Arya Repatmaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Septhian%20Ditaputra%20Raharja"> Septhian Ditaputra Raharja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The power transformer is one of the critical things in system transmission. Regular testing of the power transformer is very important to maintain the reliability of the power. One of the causes of the failure of the transformer is the breakdown of insulation caused by the presence of voids in the equipment that is electrified. As a result of the voids that occur in this power transformer equipment, it can cause partial discharge. Several methods were used to determine the occurrence of damage to the power transformer equipment, such as Sweep Frequency Response Analysis (SFRA) and Tan Delta. In Inter Bus Transformer (IBT) 500/150 kV Cirata Extra High Voltage (EHV) Substation, a breakdown occurred in the T-phase tertiary bushing. From the lessons learned in this case, a complete electrical test was carried out. From the results of the complete electrical test, there was a suspicion of deterioration in the post-breakdown SFRA results. After overhaul and inspection, traces of voids were found on the tertiary bushing, which indicated a breakdown in the tertiary bushing of the IBT 500/150kV Cirata Substation transformer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=void" title="void">void</a>, <a href="https://publications.waset.org/abstracts/search?q=bushing" title=" bushing"> bushing</a>, <a href="https://publications.waset.org/abstracts/search?q=SFRA" title=" SFRA"> SFRA</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Delta" title=" Tan Delta"> Tan Delta</a> </p> <a href="https://publications.waset.org/abstracts/158237/diagnostic-of-breakdown-in-high-voltage-bushing-power-transformer-500-kv-cirata-substation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158237.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6890</span> Parasitic Capacitance Modeling in Pulse Transformer Using FEA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Habibinia">D. Habibinia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Feyzi"> M. R. Feyzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, specialized software is vastly used to verify the performance of an electric machine prototype by evaluating a model of the system. These models mainly consist of electrical parameters such as inductances and resistances. However, when the operating frequency of the device is above one kHz, the effect of parasitic capacitances grows significantly. In this paper, a software-based procedure is introduced to model these capacitances within the electromagnetic simulation of the device. The case study is a high-frequency high-voltage pulse transformer. The Finite Element Analysis (FEA) software with coupled field analysis is used in this method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title="finite element analysis">finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=parasitic%20capacitance" title=" parasitic capacitance"> parasitic capacitance</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20transformer" title=" pulse transformer"> pulse transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency" title=" high frequency"> high frequency</a> </p> <a href="https://publications.waset.org/abstracts/31889/parasitic-capacitance-modeling-in-pulse-transformer-using-fea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">515</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6889</span> Kerr Electric-Optic Measurement of Electric Field and Space Charge Distribution in High Voltage Pulsed Transformer Oil</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hongda%20Guo">Hongda Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenxia%20Sima"> Wenxia Sima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transformer oil is widely used in power systems because of its excellent insulation properties. The accurate measurement of electric field and space charge distribution in transformer oil under high voltage impulse has important theoretical and practical significance, but still remains challenging to date because of its low Kerr constant. In this study, the continuous electric field and space charge distribution over time between parallel-plate electrodes in high-voltage pulsed transformer oil based on the Kerr effect is directly measured using a linear array photoelectrical detector. Experimental results demonstrate the applicability and reliability of this method. This study provides a feasible approach to further study the space charge effects and breakdown mechanisms in transformer oil. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20field" title="electric field">electric field</a>, <a href="https://publications.waset.org/abstracts/search?q=Kerr" title=" Kerr"> Kerr</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20charge" title=" space charge"> space charge</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20oil" title=" transformer oil"> transformer oil</a> </p> <a href="https://publications.waset.org/abstracts/48379/kerr-electric-optic-measurement-of-electric-field-and-space-charge-distribution-in-high-voltage-pulsed-transformer-oil" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6888</span> Health Assessment of Power Transformer Using Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yog%20Raj%20Sood">Yog Raj Sood</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajnish%20Shrivastava"> Rajnish Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Anchal%20Wadhwa"> Anchal Wadhwa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Power transformer is one of the electrical equipment that has a central and critical role in the power system. In order to avoid power transformer failure, information system that provides the transformer condition is needed. This paper presents an information system to know the exact situations prevailing within the transformer by declaring its health index. Health index of a transformer is decided by considering several diagnostic tools. The current work deals with UV-Vis, IFT, FP, BDV and Water Content. UV/VIS results have been pre-accessed using separate FL controller for concluding with the Furan contents. It is broadly accepted that the life of a power transformer is the life of the oil/ paper insulating system. The method relies on the use of furan analysis (insulation paper), and other oil analysis results as a means to declare health index. Fuzzy logic system is used to develop the information system. The testing is done on 5 samples of oil of transformers of rating 132/66 KV to obtain the results and results are analyzed using fuzzy logic model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interfacial%20tension%20analyzer%20%28ift%29" title="interfacial tension analyzer (ift)">interfacial tension analyzer (ift)</a>, <a href="https://publications.waset.org/abstracts/search?q=flash%20point%20%28fp%29" title=" flash point (fp)"> flash point (fp)</a>, <a href="https://publications.waset.org/abstracts/search?q=furfuraldehyde%20%28fal%29" title=" furfuraldehyde (fal)"> furfuraldehyde (fal)</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20index" title=" health index"> health index</a> </p> <a href="https://publications.waset.org/abstracts/27950/health-assessment-of-power-transformer-using-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27950.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">634</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6887</span> Condition Based Assessment of Power Transformer with Modern Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piush%20Verma">Piush Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20R.%20Sood"> Y. R. Sood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper provides the information on the diagnostics techniques for condition monitoring of power transformer (PT). This paper deals with the practical importance of the transformer diagnostic in the Electrical Engineering field. The life of the transformer depends upon its insulation i.e paper and oil. The major testing techniques applies on transformer oil and paper i.e dissolved gas analysis, furfural analysis, radio interface, acoustic emission, infra-red emission, frequency response analysis, power factor, polarization spectrum, magnetizing currents, turn and winding ratio. A review has been made on the modern development of this practical technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temperature" title="temperature">temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostics%20methods" title=" diagnostics methods"> diagnostics methods</a>, <a href="https://publications.waset.org/abstracts/search?q=paper%20analysis%20techniques" title=" paper analysis techniques"> paper analysis techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20analysis%20techniques" title=" oil analysis techniques"> oil analysis techniques</a> </p> <a href="https://publications.waset.org/abstracts/46784/condition-based-assessment-of-power-transformer-with-modern-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46784.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6886</span> Modeling of Transformer Winding for Transients: Frequency-Dependent Proximity and Skin Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yazid%20%20Alkraimeen">Yazid Alkraimeen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Precise prediction of dielectric stresses and high voltages of power transformers require the accurate calculation of frequency-dependent parameters. A lack of accuracy can result in severe damages to transformer windings. Transient conditions is stuided by digital computers, which require the implementation of accurate models. This paper analyzes the computation of frequency-dependent skin and proximity losses included in the transformer winding model, using analytical equations and Finite Element Method (FEM). A modified formula to calculate the proximity and the skin losses is presented. The results of the frequency-dependent parameter calculations are verified using the Finite Element Method. The time-domain transient voltages are obtained using Numerical Inverse Laplace Transform. The results show that the classical formula for proximity losses is overestimating the transient voltages when compared with the results obtained from the modified method on a simple transformer geometry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fast%20front%20transients" title="fast front transients">fast front transients</a>, <a href="https://publications.waset.org/abstracts/search?q=proximity%20losses" title=" proximity losses"> proximity losses</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20winding%20modeling" title=" transformer winding modeling"> transformer winding modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20losses" title=" skin losses"> skin losses</a> </p> <a href="https://publications.waset.org/abstracts/118676/modeling-of-transformer-winding-for-transients-frequency-dependent-proximity-and-skin-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118676.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6885</span> Simulation and Analytical Investigation of Different Combination of Single Phase Power Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Salih%20Taci">M. Salih Taci</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Tayebi"> N. Tayebi</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Bozk%C4%B1r"> I. Bozkır</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the equivalent circuit of the ideal single-phase power transformer with its appropriate voltage current measurement was presented. The calculated values of the voltages and currents of the different connections single phase normal transformer and the results of the simulation process are compared. As it can be seen, the calculated results are the same as the simulated results. This paper includes eight possible different transformer connections. Depending on the desired voltage level, step-down and step-up application transformer is considered. Modelling and analysis of a system consisting of an equivalent source, transformer (primary and secondary), and loads are performed to investigate the combinations. The obtained values are simulated in PSpice environment and then how the currents, voltages and phase angle are distributed between them is explained based on calculation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transformer" title="transformer">transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=equivalent%20model" title=" equivalent model"> equivalent model</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20series%20combinations" title=" parallel series combinations"> parallel series combinations</a> </p> <a href="https://publications.waset.org/abstracts/76621/simulation-and-analytical-investigation-of-different-combination-of-single-phase-power-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76621.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6884</span> Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Virender%20Singh">Virender Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mathew%20Rees"> Mathew Rees</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20Hampton"> Simon Hampton</a>, <a href="https://publications.waset.org/abstracts/search?q=Sivaram%20Annadurai"> Sivaram Annadurai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plant%20identification" title="plant identification">plant identification</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20transformer" title=" vision transformer"> vision transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/162359/plant-identification-using-convolution-neural-network-and-vision-transformer-based-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6883</span> Sea-Land Segmentation Method Based on the Transformer with Enhanced Edge Supervision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lianzhong%20Zhang">Lianzhong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Huang"> Chao Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SAR" title="SAR">SAR</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-land%20segmentation" title=" sea-land segmentation"> sea-land segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/148759/sea-land-segmentation-method-based-on-the-transformer-with-enhanced-edge-supervision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">181</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6882</span> Shifted Window Based Self-Attention via Swin Transformer for Zero-Shot Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasaswi%20Palagummi">Yasaswi Palagummi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sareh%20Rowlands"> Sareh Rowlands</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalised" title="generalised">generalised</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-shot%20learning" title=" zero-shot learning"> zero-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=inductive%20learning" title=" inductive learning"> inductive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=shifted-window%20attention" title=" shifted-window attention"> shifted-window attention</a>, <a href="https://publications.waset.org/abstracts/search?q=Swin%20transformer" title=" Swin transformer"> Swin transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20transformer" title=" vision transformer"> vision transformer</a> </p> <a href="https://publications.waset.org/abstracts/155517/shifted-window-based-self-attention-via-swin-transformer-for-zero-shot-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155517.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6881</span> Transformer Fault Diagnostic Predicting Model Using Support Vector Machine with Gradient Decent Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20O.%20Osaseri">R. O. Osaseri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Usiobaifo"> A. R. Usiobaifo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20model" title="diagnostic model">diagnostic model</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20decent" title=" gradient decent"> gradient decent</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20fault" title=" transformer fault "> transformer fault </a> </p> <a href="https://publications.waset.org/abstracts/42364/transformer-fault-diagnostic-predicting-model-using-support-vector-machine-with-gradient-decent-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6880</span> A Grey-Box Text Attack Framework Using Explainable AI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Esther%20Chiramal">Esther Chiramal</a>, <a href="https://publications.waset.org/abstracts/search?q=Kelvin%20Soh%20Boon%20Kai"> Kelvin Soh Boon Kai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human-interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques, and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally white-box in nature and not practical as they can be easily detected by humans e.g., Changing the word from “Poor” to “Rich”. We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BERT" title="BERT">BERT</a>, <a href="https://publications.waset.org/abstracts/search?q=explainable%20AI" title=" explainable AI"> explainable AI</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey-box%20text%20attack" title=" Grey-box text attack"> Grey-box text attack</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/156518/a-grey-box-text-attack-framework-using-explainable-ai" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156518.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6879</span> Time Series Forecasting (TSF) Using Various Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jimeng%20Shi">Jimeng Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahek%20Jain"> Mahek Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Giri%20Narasimhan"> Giri Narasimhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20quality%20prediction" title="air quality prediction">air quality prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20algorithms" title=" deep learning algorithms"> deep learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=look-back%20window" title=" look-back window"> look-back window</a> </p> <a href="https://publications.waset.org/abstracts/146879/time-series-forecasting-tsf-using-various-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6878</span> Simulation and Modeling of High Voltage Pulse Transformer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Emami">Zahra Emami</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Reza%20Mesgarzade"> H. Reza Mesgarzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Morad%20Ghorbami"> A. Morad Ghorbami</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Reza%20Motahari"> S. Reza Motahari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method for calculation of parasitic elements consisting of leakage inductance and parasitic capacitance in a high voltage pulse transformer. The parasitic elements of pulse transformers significantly influence the resulting pulse shape of a power modulator system. In order to prevent the effects on the pulse shape before constructing the transformer an electrical model is needed. The technique procedures for computing these elements are based on finite element analysis. The finite element model of pulse transformer is created using software "Ansys Maxwell 3D". Finally, the transformer parasitic elements is calculated and compared with the value obtained from the actual test and pulse modulator is simulated and results is compared with actual test of pulse modulator. The results obtained are very similar with the test values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pulse%20transformer" title="pulse transformer">pulse transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxwell%203D" title=" Maxwell 3D"> Maxwell 3D</a>, <a href="https://publications.waset.org/abstracts/search?q=modulator" title=" modulator"> modulator</a> </p> <a href="https://publications.waset.org/abstracts/12530/simulation-and-modeling-of-high-voltage-pulse-transformer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">458</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6877</span> Modeling and Minimizing the Effects of Ferroresonance for Medium Voltage Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hossein%20Mohammadi%20Sanjani">Mohammad Hossein Mohammadi Sanjani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashknaz%20Oraee"> Ashknaz Oraee</a>, <a href="https://publications.waset.org/abstracts/search?q=Arian%20Amirnia"> Arian Amirnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Atena%20Taheri"> Atena Taheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Arabi"> Mohammadreza Arabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmud%20Fotuhi-Firuzabad"> Mahmud Fotuhi-Firuzabad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ferroresonance effects cause overvoltage in medium voltage transformers and isolators used in electrical networks. Ferroresonance effects are nonlinear and occur between the network capacitor and the nonlinear inductance of the voltage transformer during saturation. This phenomenon is unwanted for transformers since it causes overheating, introduction of high dynamic forces in primary coils, and rise of voltage in primary coils for the voltage transformer. Furthermore, it results in electrical and thermal failure of the transformer. Expansion of distribution lines, design of the transformer in smaller sizes, and the increase of harmonics in distribution networks result in an increase of ferroresonance. There is limited literature available to improve the effects of ferroresonance; therefore, optimizing its effects for voltage transformers is of great importance. In this study, comprehensive modeling of a medium voltage block-type voltage transformer is performed. In addition, a recent model is proposed to improve the performance of voltage transformers during the occurrence of ferroresonance using damping oscillations. Also, transformer design optimization is presented in this study to show further improvements in the performance of the voltage transformer. The recently proposed model is experimentally tested and verified on a medium voltage transformer in the laboratory, and simulation results show a large reduction of the effects of ferroresonance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20transformer" title=" voltage transformer"> voltage transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=ferroresonance" title=" ferroresonance"> ferroresonance</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=damper" title=" damper"> damper</a> </p> <a href="https://publications.waset.org/abstracts/169686/modeling-and-minimizing-the-effects-of-ferroresonance-for-medium-voltage-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169686.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6876</span> Enhancing Patch Time Series Transformer with Wavelet Transform for Improved Stock Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheng-yu%20Hsieh">Cheng-yu Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Zhang"> Bo Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Hambaba"> Ahmed Hambaba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stock market prediction has long been an area of interest for both expert analysts and investors, driven by its complexity and the noisy, volatile conditions it operates under. This research examines the efficacy of combining the Patch Time Series Transformer (PatchTST) with wavelet transforms, specifically focusing on Haar and Daubechies wavelets, in forecasting the adjusted closing price of the S&P 500 index for the following day. By comparing the performance of the augmented PatchTST models with traditional predictive models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers, this study highlights significant enhancements in prediction accuracy. The integration of the Daubechies wavelet with PatchTST notably excels, surpassing other configurations and conventional models in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE). The success of the PatchTST model paired with Daubechies wavelet is attributed to its superior capability in extracting detailed signal information and eliminating irrelevant noise, thus proving to be an effective approach for financial time series forecasting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20forecasting" title=" financial forecasting"> financial forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title=" stock market prediction"> stock market prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=patch%20time%20series%20transformer" title=" patch time series transformer"> patch time series transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/186571/enhancing-patch-time-series-transformer-with-wavelet-transform-for-improved-stock-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186571.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">50</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6875</span> Analysis of the 2023 Karnataka State Elections Using Online Sentiment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pranav%20Gunhal">Pranav Gunhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=Karnataka%20elections" title=" Karnataka elections"> Karnataka elections</a>, <a href="https://publications.waset.org/abstracts/search?q=congress" title=" congress"> congress</a>, <a href="https://publications.waset.org/abstracts/search?q=BJP" title=" BJP"> BJP</a>, <a href="https://publications.waset.org/abstracts/search?q=transformers" title=" transformers"> transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=Indic%20languages" title=" Indic languages"> Indic languages</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=novel%20architectures" title=" novel architectures"> novel architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=IndicBERT" title=" IndicBERT"> IndicBERT</a>, <a href="https://publications.waset.org/abstracts/search?q=lok%20sabha%20elections" title=" lok sabha elections"> lok sabha elections</a> </p> <a href="https://publications.waset.org/abstracts/169101/analysis-of-the-2023-karnataka-state-elections-using-online-sentiment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169101.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">85</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6874</span> Reduction of Planar Transformer AC Resistance Using a Planar Litz Wire Structure </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Belloumi">Hamed Belloumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aymen%20Ammouri"> Aymen Ammouri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ferid%20Kourda"> Ferid Kourda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new trend in power converters is to design planar transformer that aim for low profile. However, at high frequency, the planar transformer ac losses become significant due to the proximity and skin effects. In this paper, the design and implementation of a novel planar litz conductor is presented in order to equalize the flux linkage and improving the current distribution. The developed PCB litz wire structure minimizes the losses in a similar way to the conventional multi stranded litz wires. In order to further illustrate the eddy current effect in different arrangements, a finite-element analysis (FEA) tool is used to analyze current distribution inside the conductors. Finally, the proposed planar transformer has been integrated in an electronic stage to test at high signal levels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=planar%20transformer" title="planar transformer">planar transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=finite-element%20analysis%20%28FEA%29" title=" finite-element analysis (FEA)"> finite-element analysis (FEA)</a>, <a href="https://publications.waset.org/abstracts/search?q=winding%20losses" title=" winding losses"> winding losses</a>, <a href="https://publications.waset.org/abstracts/search?q=planar%20litz%20wire" title=" planar litz wire"> planar litz wire</a> </p> <a href="https://publications.waset.org/abstracts/29429/reduction-of-planar-transformer-ac-resistance-using-a-planar-litz-wire-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29429.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6873</span> Comparison between FEM Simulation and Experiment of Temperature Rise in Power Transformer Inner Steel Plate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byung%20hyun%20Bae">Byung hyun Bae</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In power transformer, leakage magnetic flux generate temperature rise of inner steel plate. Sometimes, this temperature rise can be serious problem. If temperature of steel plate is over critical point, harmful gas will be generated in the tank. And this gas can be a reason of fire, explosion and life decrease. So, temperature rise forecasting of steel plate is very important at the design stage of power transformer. To improve accuracy of forecasting of temperature rise, comparison between simulation and experiment achieved in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20transformer" title="power transformer">power transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20plate" title=" steel plate"> steel plate</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20rise" title=" temperature rise"> temperature rise</a>, <a href="https://publications.waset.org/abstracts/search?q=experiment" title=" experiment"> experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/12749/comparison-between-fem-simulation-and-experiment-of-temperature-rise-in-power-transformer-inner-steel-plate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">495</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transformer%20models&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transformer%20models&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transformer%20models&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=transformer%20models&page=5">5</a></li> <li class="page-item"><a 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