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- W4362631419 abstract "Power quality is the quality of electrical energy in the power system. And the power quality disturbances (PQDs) in the power system lead to severe consequences for the equipment's life and the operator's safety. The accurate disturbance identification is a crucial prerequisite to managing power quality. Currently, the identification accuracy of PQDs needs to be improved, especially in high-intensity noise environments. In this paper, a novel power quality identification method based on adaptive symplectic geometric mode decomposition (ASGMD) and tent chaotic map and adaptive parameter method improved marine predators algorithm (IMPA) optimization is proposed. Firstly, the PQDs are pre-processed by adaptive symplectic geometric mode decomposition with K-means, fast Fourier transform, coefficient of kurtosis, and wavelet thresholding to reduce the noise component. Secondly, twenty-three PQDs features of the wavelet transform, fast Fourier transform, and mathematical statistics are optimally selected by IMPA to improve the identification accuracy and efficiency. Finally, the PQDs with selected features are recognized by the extreme learning machine with optimized weights and biases. IMPA improves the uniformity of the initial population distribution and the optimization process of three iterative phases, thus promoting the search accuracy of the optimal features, input weights, and biases. The proposed method has more accurate identification accuracy and fast identification speed by simulating different noise intensity environments than other methods. Remarkably, the identification accuracy reaches 91% in high-intensity noise environments of 10dB This method has practical significance for PQDs identification of large areas, long periods, and high noise-resistant." @default.
- W4362631419 created "2023-04-07" @default.
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- W4362631419 date "2023-07-01" @default.
- W4362631419 modified "2023-10-03" @default.
- W4362631419 title "Power quality disturbances identification based on adaptive symplectic geometric mode decomposition and improved marine predators algorithm" @default.
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- W4362631419 doi "https://doi.org/10.1016/j.epsr.2023.109365" @default.
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