Classification of Transformer Abnormal Current Types Using Machine Learning
碩士 === 國立臺灣科技大學 === 電機工程系 === 107 === This thesis using machine learning to classify the abnormal current types of transformer. When the transformer connect to the power system or when the external faults is cleared, the residual magnetic flux cause the transformer core to saturate and may cause a t...
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ndltd-TW-107NTUS54420302019-10-23T05:46:02Z http://ndltd.ncl.edu.tw/handle/96f3x3 Classification of Transformer Abnormal Current Types Using Machine Learning 運用機器學習於變壓器異常電流類型之辨識 Chieh-Chun Hsiao 蕭傑駿 碩士 國立臺灣科技大學 電機工程系 107 This thesis using machine learning to classify the abnormal current types of transformer. When the transformer connect to the power system or when the external faults is cleared, the residual magnetic flux cause the transformer core to saturate and may cause a transient electromagnetic inrush current. The inrush current is different from the external faults current, so how to classify the abnormal current types, and prevent the protection relay to malfunctioning is important. Inrush current and external faults current have different characteristics, and these characteristics are regarded as input data of neural network. The transfer function of neural network processing unit adopts tangent hyperbolic function, and is structured by the multi-layer feedforward network. In order to classify the abnormal current types of the transformer. This thesis using Matlab Simulink to simulate the inrush current and the external faults of the transformer. The simulated currents using discrete wavelet transform to capture the important data of the signals, and then the statistical features are calculated as the input data of neural network. After the neural network learning and recalling. Finally it can classify the abnormal current types of the transformer. The method proposed in this thesis is feasible, and the classification results have good accuracy. Cheng-Chien Kuo 郭政謙 2019 學位論文 ; thesis 107 zh-TW |
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碩士 === 國立臺灣科技大學 === 電機工程系 === 107 === This thesis using machine learning to classify the abnormal current types of transformer. When the transformer connect to the power system or when the external faults is cleared, the residual magnetic flux cause the transformer core to saturate and may cause a transient electromagnetic inrush current. The inrush current is different from the external faults current, so how to classify the abnormal current types, and prevent the protection relay to malfunctioning is important. Inrush current and external faults current have different characteristics, and these characteristics are regarded as input data of neural network. The transfer function of neural network processing unit adopts tangent hyperbolic function, and is structured by the multi-layer feedforward network. In order to classify the abnormal current types of the transformer.
This thesis using Matlab Simulink to simulate the inrush current and the external faults of the transformer. The simulated currents using discrete wavelet transform to capture the important data of the signals, and then the statistical features are calculated as the input data of neural network. After the neural network learning and recalling. Finally it can classify the abnormal current types of the transformer. The method proposed in this thesis is feasible, and the classification results have good accuracy.
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Cheng-Chien Kuo |
author_facet |
Cheng-Chien Kuo Chieh-Chun Hsiao 蕭傑駿 |
author |
Chieh-Chun Hsiao 蕭傑駿 |
spellingShingle |
Chieh-Chun Hsiao 蕭傑駿 Classification of Transformer Abnormal Current Types Using Machine Learning |
author_sort |
Chieh-Chun Hsiao |
title |
Classification of Transformer Abnormal Current Types Using Machine Learning |
title_short |
Classification of Transformer Abnormal Current Types Using Machine Learning |
title_full |
Classification of Transformer Abnormal Current Types Using Machine Learning |
title_fullStr |
Classification of Transformer Abnormal Current Types Using Machine Learning |
title_full_unstemmed |
Classification of Transformer Abnormal Current Types Using Machine Learning |
title_sort |
classification of transformer abnormal current types using machine learning |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/96f3x3 |
work_keys_str_mv |
AT chiehchunhsiao classificationoftransformerabnormalcurrenttypesusingmachinelearning AT xiāojiéjùn classificationoftransformerabnormalcurrenttypesusingmachinelearning AT chiehchunhsiao yùnyòngjīqìxuéxíyúbiànyāqìyìchángdiànliúlèixíngzhībiànshí AT xiāojiéjùn yùnyòngjīqìxuéxíyúbiànyāqìyìchángdiànliúlèixíngzhībiànshí |
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1719276328337801216 |