Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition

• Published in: IEEE transactions on industrial informatics Vol. 15; no. 5; pp. 2915 - 2926
• Main Authors: Zhao, Chen, Li, Kaicheng, Li, Yuanzheng, Wang, Lingyun, Luo, Yi, Xu, Xuebin, Ding, Xiaojun, Meng, Qingxu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Classifiers; Decomposition; discriminative projection; extreme learning machine (ELM); Feature extraction; Machine learning; multilabel; multiple power quality disturbance (MPQD); Neural networks; Particle swarm optimization; Pattern classification; Power engineering computing; Power quality; Power system faults; Projection; Recognition; Support vector machines; variational mode decomposition (VMD)

Power quality events are usually associated with more than one disturbance and their recognition is typically based on multilabel learning. In this study, we propose a new method for recognizing multiple power quality disturbances (MPQDs) based on variational mode decomposition (VMD) and a random di...