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)
• DOI: 10.1109/TII.2018.2871253
• ISSN: 1551-3203

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 discriminative projection extreme learning machine for multilabel learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multilabel classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using MATLAB synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with the several recent state-of-the-art multilabel learning algorithms, RDPEML achieved better classification performance with superior computational speed.