A power quality detection and classification algorithm based on FDST and hyper-parameter tuned light-GBM using memetic firefly algorithm

•PQ disturbances (PQDs) detection and classification using FDST and LightGBM (LGBM)•The hyper-parameters of LGBM are optimized through memetic firefly algorithm.•Performance is validated on 25 types of PQDs (both single and multiple events)•The overall detection accuracy is found to be 99.714% with...

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Veröffentlicht in:	Measurement : journal of the International Measurement Confederation Jg. 187; S. 110260
Hauptverfasser:	Panigrahi, Rasmi Ranjan, Mishra, Manohar, Nayak, Janmenjoy, Shanmuganathan, Vimal, Naik, Bighnaraj, Jung, Young-Ae
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Elsevier Ltd 01.01.2022 Elsevier Science Ltd
Schlagworte:	Algorithms Classification Classifiers Comparative studies Disturbances Electric power systems Fast discrete s-trasform Feature extraction Genetic algorithms Heuristic methods IEEE-1159 standard Light GBM Nonlinear systems Power quality PSO Sensors Waveforms EFB PQ ANN MAT fi y^j EPQ ELM STFT MFA KNN SVM Light GBM DT yj XGBM HHT Classification Λ WT EMD Fast discrete s-trasform ST PQD LGBM GOSS PSO IEEE-1159 standard MAF Power quality η Feature extraction FE FDST l(·)
ISSN:	0263-2241, 1873-412X
Online-Zugang:	Volltext
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