The mining of high risk equipment based on the algorithm of HR-Tree’s decision

• Autorzy: Wang Shanyuan , Zhang Yujie , Li Yao , Gao Suisui , Yang Feiling

Identyfikatory

DOI

10.29354/diag/122341

• Języki publikacji: EN

Abstrakty

EN

Due to the different construction of various subsystems in the power grid, the information of various systems are not closely connected. Nowadays, the network is complex and changeable where the automation is getting higher. This article takes high-risk equipment set of substation in Liaoyang as the research background. It constructs HR-Tree for the device set, and establishes a high-risk equipment evaluation system which based on the HR-Tree context. Then we calculate high-risk equipment sets in the structure of overall data set. By establishing the original data set and the prior knowledge system of equipment risk, the non-candidate high-risk equipment set is reduced in the local path of the high-risk equipment set. We refer to the process of reducing data as minus branch. After the threshold is established, the branches are reduced and the highest risk equipment set is obtained. Finally, we use the scoring system to find the probability of occurrence of associated devices, such information is more open. Example showed that such methods could effectively express high-risk device sets, and managers could get early warning information based on this. It helps people monitoring the power system, w hich could also provides new ideas for the monitoring project.

Słowa kluczowe

EN

high-risk equipment set; HR-Tree; minus branch; early warning

PL

HR-Tree; system elektroenergetyczny; monitorowanie; wczesne ostrzeganie

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2020
• Tom: Vol. 21, No. 2
• Strony: 51--59
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Wang Shanyuan

• Northeastern University, College of Information Science and Engineering, Institute of Electrical Automation, 110004

autor

Zhang Yujie

• Northeastern University, College of Information Science and Engineering, Institute of Electrical Automation, 110004

autor

Li Yao

• Northeastern University, College of Information Science and Engineering, Institute of Electrical Automation, 110004

autor

Gao Suisui

• Northeastern University, College of Information Science and Engineering, Institute of Electrical Automation, 110004

autor

Yang Feiling

• Northeastern University, College of Information Science and Engineering, Institute of Electrical Automation, 110004

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 (2021).