Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 9, Issue 5, Pages 4838-4846Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2017.2672881
Keywords
Microgrid; fault classification; particle swarm optimization; supervised machine learning
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This paper introduces a new approach that uses a combination of wavelet functions and machine learning for fault classification in microgrids (MGs). Particle swarm optimization is applied to identify the optimal wavelet functions combination that serves as a matching pursuit to extract the most prominent features, which are hidden in the current/voltage waveforms when applying the discrete wavelet transform. Four different classification techniques (i.e., decision tree, K-nearest neighbor, support vector machine, and Naive Bayes) are used to automate the procedure of fault classification in MGs and their performances are statistically compared. The consortium for electric reliability technology solutions (CERTS) MG is used to exemplify the effectiveness of the proposed approach after modeling the MC system in power systems computer aided design/electromagnetic transient direct current (PSCAD/EMTDC) software package. The results are presented, discussed, and conclusions are drawn.
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