Study of Lightning Arrester Resistive Current Prediction Based on GRU-Dropout Network
MOA(metal oxide arrester)resistive current has the role of guiding,shunting and protecting during equip-ment overvoltage,and its accurate prediction can effectively ensure the safety and stability of the power system.A MOA resistive current prediction method based on GRU(gated recurrent unit)combined with Dropout(stochastic deac-tivation)is proposed to address the drawbacks of overfitting and insufficient generalization ability of some single net-works.The network is trained and analyzed by combining the actual current data of the substation and setting ap-propriate hyperparameters.The experimental results show that the use of GRU-Dropout network improves the general-ization ability and reduces overfitting compared with LSTM.Improves the accuracy compared with RNN and BP neu-ral networks,with its optimal average absolute error and root mean square error of 1.98%and 2.73%.It can predict the trend of MOA insulation to ground more accurately in practical applications,and has certain application value.
metal oxide arrester(MOA)resistive currentgated neural networkstochastic deactivatio