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Fault Diagnosis Method of Intelligent Substation Based on Improved Association Rule Mining Algorithms

  • Li ChenEmail author
  • Liangyi Wang
  • Qian He
  • Hui Liu
Conference paper
  • 91 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)

Abstract

With the digitalization and intellectualization of substations, the amount of state data collected by intelligent components is becoming larger and larger. Traditional data mining technology cannot meet the requirements of real-time data processing and application speed. For a typical smart substation, a fault diagnosis method based on improved association rule mining algorithm is proposed for transformer near-zone fault analysis. Firstly, an improved association rule data mining algorithm based on rough set is designed to extract information from massive data and diagnose faults. Then genetic algorithm is applied to improve association rule data mining algorithm to speed up data processing. Finally, simulation analysis is carried out to demonstrate the effectiveness and rapidity of the proposed method. The results show that the intelligent substation fault diagnosis method based on improved association rule mining algorithm has fast and powerful reduction ability in data processing. It can extract useful data from intelligent substation components to accurately judge fault information, reduce data scale and improve fault processing speed.

Keywords

Intelligent substation Improved association rules Rough set Genetic algorithms Intelligent electronic device 

Notes

Acknowledgements

This work was supported by Science and Technology Project of State Grid Chongqing Electric Power Company in 2018. The project name is “Integrated Operational Support Technology of Intelligent Substations Based on Total Service Data” (No. 2018#35).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.State Grid Chongqing Electric Power CompanyChongqingChina

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