Research on state evaluation and risk assessment for relay protection system based on machine learning algorithm. Issue 18 (4th August 2020)

• Record Type: Journal Article
• Authors: Ying, Liming; Jia, Yongtian; Li, Wenan
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 18(2020)
• Page Start: 3619
• Page End: 3629
• Publication Date: 2020-08-04
• Subjects: learning (artificial intelligence) -- relay protection -- fuzzy set theory -- regression analysis -- power engineering computing -- power system protection; reliable quantitative basis -- relay protection systems -- Mahalanobis distance machine -- analytic hierarchy process fuzzy synthetic evaluation -- supervised multiple regression analysis algorithm -- unsupervised K‐means algorithm -- state evaluation -- risk assessment -- relay protection system -- machine learning algorithm -- stable operation -- power systems -- operation data; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6552
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The relay protection system plays an important role in ensuring the stable operation of power systems. Combined with operation data collected from a region in China, this study is aimed at providing a reliable quantitative basis for relay protection systems' operating maintenance by the aid of a semi‐supervised Mahalanobis distance machine learning algorithm. The evaluation result is first applied as a training set on the basis of the analytic hierarchy process fuzzy synthetic evaluation. Then, contrastive analysis is conducted in terms of accuracy, processing time, and feasibility. It includes comparative cases with a supervised multiple regression analysis algorithm and unsupervised K‐means algorithm. The comparison result reveals that the algorithm can effectively and accurately predict the running state of the equipment and offer a quantitative reference for relative maintenance strategy.