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

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.