| DOI | Resolve DOI: https://doi.org/10.1109/SDPC.2018.8664949 |
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| Author | Search for: Li, Anyi; Search for: Yang, Xiaohui; Search for: Dong, Huanyu; Search for: Yang, Chunsheng1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC), August 15-17, 2018, Xi'an, China |
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| Subject | power transformer; cuckoo search; BP neural network; multi-hidden layer; fault diagnosis |
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| Abstract | An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM systems enable a pro-active fault prevention strategy through continuously monitoring the health of complex systems. Power transformer PHM will play a key role in securing and stabling electrical power supply to users, especially in the smart grid. In this paper, we present a novel approach for power transformer fault diagnosis based on cuckoo search optimized neural network, also named it as dissolved gas analysis (DGA) approach. The proposed approach uses the Cuckoo Search (CS) algorithm to select the best parameters of backpropagation (BP) neural network, which can approximate any nonlinear relationships. The paper validates the usefulness and efficiency of the proposed approach by conducting simulation to compare the results to Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The results demonstrated that the proposed approach outperformed other methods such as BP neural network, SVM, GA-BP, and PSO-BP. It significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM. |
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| Publication date | 2019-03-14 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 24287b87-27c0-4c21-ae60-b0f759487350 |
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| Record created | 2019-05-17 |
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| Record modified | 2020-03-16 |
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