A real-time power quality events recognition using variational mode decomposition and online-sequential extreme learning machine

Abstract In this paper, variational mode decomposition (VMD) and online-sequential extreme learning machine (OSELM) are integrated to detect and classify power quality events (PQEs) in real-time. Empirical Wavelet transform (EWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to decompose the non-stationary power quality (PQ) signals into intrinsic mode functions (IMFs) or band-limited mode of oscillations. Four felicitous features are extracted by applying the Hilbert transform (HT) on the decomposed PQE signals. The synthetic, as well as practical PQE signals, are considered to test and examine the overall performance of the proposed method. OSELM is an efficient and advanced classifier which is implemented to recognize the single as well as multiple PQEs. The robust anti-noise performance, faster learning speed, lesser computational complexity, superior classification accuracy and short event detection time prove that the proposed VMD-OSELM method can be implemented in the electrical power system. 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.