基于长短时记忆神经网络在故障故障中参数依靠经验确定，具有不确定性，从而

Based on the fact that the parameters of the long and short-term memory neural network are determined by experience in the faults and are uncertain, which leads to the defects of the model's fault diagnosis accuracy rate being reduced, this paper uses an improved gray wolf algorithm to optimize the long-short-term memory neural network and proposes a LSTM network parameter optimization OLTC fault diagnosis method based on improved gray wolf algorithm

Based on the parameters of long-term memory neural network in fault, which is determined by experience and has uncertainty, which leads to the reduction of the fault accuracy of the model, this paper optimizes the long-term memory neural network by using the improved gray wolf algorithm, and proposes an OLTC fault diagnosis method based on the LSTM network parameter optimization based on the improved gray wolf algorithm

Based on the fact that the parameters of long-term and short-term memory neural network are determined by experience and uncertain, which leads to the decrease of fault diagnosis accuracy of the model, an improved grey Wolf algorithm is used to optimize the long-term and short-term memory neural network, and an OLTC fault diagnosis method based on Improved Grey Wolf algorithm is proposed<br>