Journal
IET GENERATION TRANSMISSION & DISTRIBUTION
Volume 14, Issue 18, Pages 3619-3629Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2018.6552
Keywords
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
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Funding
- key fund research project of China Southern Power Grid [GZ2014-2-0049]
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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.
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