A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine |
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| Affiliation: | 1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China;2. Xiamen Power Supply Company of State Grid Fujian Electric Power Co., Ltd, Xiamen, Fujian 361004, China;1. Laboratory for Artificial Intelligence in Design, Hong Kong, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;1. School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China;2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, PR China;3. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, PR China;1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;2. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China;3. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China |
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| Abstract: | In order to solve the problems of unsatisfactory diagnosis performance and unstable model of conventional fault diagnosis methods for transformers, a new approach based on improved empirical wavelet transform (IEWT) and salp swarm algorithm (SSA) optimized kernel extreme learning machine (KELM) is proposed in this study. Firstly, IEWT is used to adaptively decompose the vibration signal to obtain a set of empirical wavelet functions (EWFs). Secondly, the first n-order components with high correlation coefficient are collected. Thirdly, the mean value, variance, kurtosis, refine composite multiscale entropy (RCMSE), and time-frequency entropy(TFE) of these n-order components are calculated to construct a fusion feature vector. Finally, a two-level diagnostic model based on SSA-KELM is established. The first-level of it is applied to identify normal and abnormal states, and the second-level is selected to identify fault categories in the abnormal states. The proposed method can effectively diagnose the existing fault categories in the training set and accurately identify the unknown categories of faults. Experimental results show that the proposed method can efficiently extract features of different vibration signals and identify the faults, with an average classification accuracy of 96.25%. It is better than other methods, such as wavelet packet energy spectrum analysis-KELM and EWT-fisher. |
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| Keywords: | Transformer Fault diagnosis Improved empirical wavelet transform (IEWT) Kernel extreme learning machine (KELM) Salp swarm algorithm (SSA) |
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