| Improved BP Neural Network for Transformer Fault Diagnosis |
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| 引用本文： | SUN Yan-jing ZHANG Shen MIAO Chang-xin LI Jing-meng. Improved BP Neural Network for Transformer Fault Diagnosis[J]. 中国矿业大学学报(英文版), 2007, 17(1): 138-142. DOI: 10.1016/S1006-1266(07)60029-7 |
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| 作者姓名： | SUN Yan-jing ZHANG Shen MIAO Chang-xin LI Jing-meng |
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| 作者单位： | School of Information and Electrical Engineering, China University of Mining ＆ Technology, Xuzhou, Jiangsu 221008, China |
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| 摘 要： | The back propagation （BP）-based artificial neural nets （ANN） can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM （BP with Adaptive Learning Rate and Momentum coefficient） algorithm is proposed to reduce the training time and avoid being trapped into local minima, where the learning rate and the momentum coefficient are altered at iterations. We developed a system of transformer fault diagnosis based on Dissolved Gases Analysis （DGA） with a BP-ALM algorithm. Training patterns were selected from the results of a Refined Three-Ratio method （RTR）. Test results show that the system has a better ability of quick learning and global convergence than other methods and a superior performance in fault diagnosis compared to convectional BP-based neural networks and RTR.
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| 关 键 词： | 人工神经网络 反向传播 石油 模糊控制 |
| 收稿时间： | 2006-06-30 |
| 修稿时间： | 2006-07-20 |
Improved BP Neural Network for Transformer Fault Diagnosis |
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| SUN Yan-jing,ZHANG Shen,MIAO Chang-xin,LI Jing-meng. Improved BP Neural Network for Transformer Fault Diagnosis[J]. Journal of China University of Mining and Technology, 2007, 17(1): 138-142. DOI: 10.1016/S1006-1266(07)60029-7 |
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| Authors: | SUN Yan-jing ZHANG Shen MIAO Chang-xin LI Jing-meng |
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| Affiliation: | School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China |
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| Abstract: | The back propagation (BP)-based artificial neural nets (ANN) can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and Momentum coefficient) algorithm is proposed to reduce the training time and avoid being trapped into local minima, where the learning rate and the momentum coefficient are altered at iterations. We developed a system of transformer fault diagnosis based on Dissolved Gases Analysis (DGA) with a BP-ALM algorithm. Training patterns were selected from the results of a Refined Three-Ratio method (RTR). Test results show that the system has a better ability of quick learning and global convergence than other methods and a superior performance in fault diagnosis compared to convectional BP-based neural networks and RTR. |
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| Keywords: | transformer fault diagnosis back-propagation artificial neural network momentum coefficient |
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