Abstract
A microgrid fault diagnosis method based on whale algorithm optimizing extreme learning machine (ELM) is proposed. Firstly, the three-phase fault voltage is analyzed by wavelet packet decomposition, and the feature vector composed of wavelet packet energy entropy is calculated as data samples. Then, a whale algorithm is used to optimize the extreme learning machine to establish a diagnostic model to identify and diagnose the fault type of microgrid. The whale algorithm has the characteristics of simple parameter setting, fast learning speed, and strong global optimization ability. The whale algorithm is used to optimize the input weights and hidden layer neuron thresholds of the extreme learning machine, which solves the problem that the random initialization of the input weights and hidden layer neuron thresholds easily affects the network performance, which can further improve the learning speed and generalization ability of the network, and benefit to global optimization. Simulation results show that compared with BP neural network, RBF neural network and ELM, the fault diagnosis model based on whale algorithm optimization extreme learning machine has faster learning speed, stronger generalization ability and higher recognition accuracy.
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Wu, Z., Lu, X. Microgrid Fault Diagnosis Based on Whale Algorithm Optimizing Extreme Learning Machine. J. Electr. Eng. Technol. 19, 1827–1836 (2024). https://doi.org/10.1007/s42835-023-01670-1
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DOI: https://doi.org/10.1007/s42835-023-01670-1