Variational mode decomposition and weighted online sequential extreme learning machine for power quality event patterns recognition

Abstract In this paper, variational mode decomposition (VMD) and a newly developed weighted online sequential extreme learning machine (WOSELM) are integrated to detect and classify the power quality events (PQEs) in real-time. The feasibility of VMD is validated by applying on PQEs (such as harmonic and flicker) for the estimation of magnitude, phase,and frequency. Estimated results prove the usefulness of VMD and further four efficacious power quality indices of the band-limited intrinsic mode functions (BLIMFs) are extracted. The indices are used for the classification of single and multiple PQEs using different advanced classifiers. The recognition architecture of variational mode decomposition with weighted online sequential extreme learning machine (VMD-WOSELM) is tested and compared withother methods. The robust anti-noise performance, faster learning speed, lesser computational complexity, superior classification accuracy and short event detection time prove that the proposed VMD-WOSELM method can be implemented in electrical power systems. Finally, a PC interface based hardware prototype is developed to verify the cogency of the proposed method in real time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.

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