Power quality disturbances identification based on adaptive symplectic geometric mode decomposition and improved marine predators algorithm

• Published in: Electric power systems research 2023-07, Vol.220, p.109365, Article 109365
• Main Authors: Ni, Chenyi, Chen, Hao, Chen, Yibiao, Yao, Yao, Li, Lele
• Format: Article
• Language: English
• Subjects: Adaptive symplectic geometric mode decomposition; Features optimization; Improved extreme learning machine; Improved marine predators algorithm; Power quality disturbances
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2023.109365

•The proposed method adopts K-means, FFT, and coefficient of kurtosis to improve the signal preprocessing ability of SGMD, which makes different types of disturbance features easy to be extracted.•It adopts an optimization algorithm to optimize the feature extraction of multiple methods and the disturbance identification of ELM, which improves the accuracy of feature extraction and disturbance identification.•It adopts tent chaotic mapping and adaptive parameters to further improve the optimization performance of MPA. 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.