BA-PNN-based methods for power transformer fault diagnosis |
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| Affiliation: | 1. College of Information Engineering, Nanchang University, Nanchang 330031, Jiangxi, China;2. National Research Council Canada, Ottawa K1A 0R6, Canada;1. Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi 530004, China;2. State Grid Henan Electric Power Research Institute, Zhengzhou, Henan 450052, China;3. Shijiazhuang Power Supply Branch of State Grid Electric Power Company, Shijiazhuang 050093, China;4. Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT 06516, USA;5. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China;6. National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning, Guangxi 530004, China;1. Key Laboratory of Power System Intelligent Dispatch and Control (Shandong University), Ministry of Education, Jinan 250061, China;2. CSG Power Dispatching Control Center, Guangzhou 510663, China;3. State Grid Shandong Electric Power Maintenance Company, Jinan 250118, China;1. College of Engineering and Technology, Southwest University, Chongqing 400715, China;2. International R & D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing 400715, China |
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| Abstract: | This paper presents a machine learning-based approach to power transformer fault diagnosis based on dissolved gas analysis (DGA), a bat algorithm (BA), optimizing the probabilistic neural network (PNN). PNN is a radial basis function feedforward neural network based on Bayesian decision theory, which has a strong fault tolerance and significant advantages in pattern classification. However, one challenge still remains: the performance of PNN is greatly affected by its hidden layer element smooth factor which impacts the classification performance. The proposed approach addresses this challenge by deploying the BA algorithm, a kind of bio-inspired algorithm to optimize PNN. Using the real data collected from a transformer system, we conducted the experiments for validating the performance of the developed method. The experimental results demonstrated that BA is an effective algorithm for optimizing PNN smooth factor and BA-PNN can improve the fault diagnosis performance; in turn, and the machine learning-based model (BA-PNN) can significantly enhance the accuracies of power transformer fault diagnosis. |
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| Keywords: | Bat algorithm Probability neural network Smooth factor Power transformer Fault diagnosis |
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