Abstract：The complexity and openness of smart grids make them more vulnerable to the threat of cyber attacks when exchanging information. Most of the current detection methods only focus on detecting the existence of the attack and cannot determine the specific location of the attacked distributed power supply, which results in the inability to quickly isolate the attacked distributed power supply and subsequently cause serious damage. To propose a false data injection attack detection method for AC microgrids based on the artificial rabbit swarm optimization algorithm optimizing a multi-core limit learning machine. A combined kernel function is introduced into the traditional extreme learning machine to improve the learning ability and generalization ability of the detection model, and the artificial rabbit pack optimization algorithm with strong global search ability is used to optimize the kernel function parameters and regularization coefficients of the multi-core extreme learning machine to further improve the detection accuracy of the detection model. Simulation verification is carried out using step attack signals with amplitudes of 55 and 95 within the non-training samples, and the detection accuracy ranges from (93.44~94.64)% and (98.11~99.23)%, respectively, and comparative analyses are carried out with other detection models to verify the superiority of the proposed method.
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2024, Vol. 45 
