Power System Fault Detection Method Based on Adaptive Machine Learning
A power system fault detection method based on recurrent neural network(RNN)is proposed for fault detection in power systems,and the Adam optimization algorithm is introduced for network model optimization.Firstly,the overall framework of power system fault detection based on RNN model was introduced,and then the basic structure of RNN was studied,including input,hidden state,weight matrix,etc.To further optimize the model,Adam optimization was introduced,which can improve the robustness and generalization ability of the model by adjusting the adaptive learning rate.The results show that the Adam optimization algorithm has a significant effect on improving the performance of the model.Compared with the original model,the proposed method significantly improves accuracy,recall,and F1 score,making it an advanced and effective method in power system fault detection.
power systemrecurrent neural networkAdam optimization algorithm
NETL
NSTL
万方数据
