Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion

• Autorzy: Zeng Xin , Xiong Xingzhong , Luo Zhongqiang

Identyfikatory

DOI

10.24425/ijet.2021.135956

• Języki publikacji: EN

Abstrakty

EN

In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multi-source information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequency-domain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multi-feature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods.

Słowa kluczowe

EN

information entropy; Bayesian network; multi-source information fusion; D-S evidence theory; fault diagnosis

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2021
• Tom: Vol. 67, No. 2
• Strony: 143--148
• Opis fizyczny: Bibliogr. 13 poz., schem., tab., wykr.

Twórcy

autor

Zeng Xin

• School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China

autor

Xiong Xingzhong

• School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China

autor

Luo Zhongqiang

• School of Automation and Information Engineering and Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China

Bibliografia

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• [4] Jianfeng Zhou, Genserik Reniers and Laobing Zhang, ”A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry”, Chemical Engineering Science, 2017, 174, 136-145.
• [5] Sen Wang and Xiaorun Li, ”Circuit Breaker Fault Detection Method Based on Bayesian Approach”, Industrial Control Computer, 2018, 31(4), 147-151.
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• [8] Bangcheng Lai and Genxiu Wu, ”The Evidence Combination Method Based on Information Entropy”, Journal of Jiangxi Normal University (Natural Science), 2012, 36(5), 519-523.
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• [10] Juan Yan, Minfang Peng, et al., ”Fault Diagnosis of Grounding Grids Based on Information Entropy and Evidence Fusion”, Proceedings of the CSU-EPSA , 2017, 29(12), 8-13.
• [11] Ershadi, Mohammad Mahdi and Seifi, Abbas, ”An efficient Bayesian network for differential diagnosis using experts’ knowledge”, International Journal of Intelligent Computing and Cybernetics, 2020, 13(1), 103-126.
• [12] Guan Li, Zhifeng Liu, Ligang Cai and Jun Yan, ”Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory”, Sensors, 2020, 20(4), 1-17.
• [13] Xiaofei He, Xiaoyang Tong and Shu Zhou, ”Power system fault diagnosis based on Bayesian network and fault section location”, Power system protection and control, 2010, 38(12), 29-34

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).