NDLI: Research on state evaluation and risk assessment for relay protection system based on machine learning algorithm

Content Provider	IET Digital Library
Author	Ying, Liming Jia, Yongtian Li, Wenan
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.
Starting Page	3619
Ending Page	3629
Page Count	11
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 18, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/18
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.6552
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-04-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Analytic Hierarchy Process Fuzzy Synthetic Evaluation Combinatorial Mathematics Data Handling Technique Fuzzy Set Theory Knowledge Engineering Technique Learning in AI Machine Learning Algorithm Mahalanobis Distance Machine Operation Data Power Engineering Computing Power System Power System Protection Regression Analysis Relay Protection Relay Protection System Reliable Quantitative Basis Risk Assessment Stable Operation State Evaluation Statistics Supervised Multiple Regression Analysis Algorithm Unsupervised K-means Algorithm
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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