Research on operation fault diagnosis algorithm of power grid equipment based on power big data

• Autorzy: Qian Jianguo , Zhu Bingquan , Li Ying , Shi Zhengchai

Identyfikatory

DOI

10.24425/aee.2020.134630

• Języki publikacji: EN

Abstrakty

EN

Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.

Słowa kluczowe

EN

association rules; big data; data mining; fault diagnosis; grid equipment

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2020
• Tom: Vol. 69, nr 4
• Strony: 793--800
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wz.

Twórcy

autor

Qian Jianguo

• Equipment Monitoring Department, State Grid Zhejiang Electric Power Company China

autor

Zhu Bingquan

• Equipment Monitoring Department, State Grid Zhejiang Electric Power Company China

autor

Li Ying

• Equipment Monitoring Department, State Grid Zhejiang Electric Power Company China

autor

Shi Zhengchai

• Wenzhou Power Supply Company, State Grid Zhejiang Electric Power Company China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).