Abstract:Recently, long short-term memory (LSTM) networks have been widely adopted to help with fault diagnosis for power systems. However, the parameters of LSTM networks are determined by prior knowledge and experience and thereby not capable of dealing with unexpected faults in volatile environments. In this paper, we propose and apply an improved grey wolf optimization (IGWO) algorithm to optimize the parameters of LSTM networks, aiming to circumvent the drawback of empirical LSTM parameters and enhance the fault d… Show more
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