Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM

Abstract

Power system faults are significant problems in power transmission and distribution. Methods based on relay protection actions and electrical component actions have been put forward in recent years. However, they have deficiencies dealing with power system fault. In this paper, a method for data-based line trip fault prediction in power systems using long short-term memory (LSTM) networks and support vector machine (SVM) is proposed. The temporal features of multisourced data are captured with LSTM networks, which perform well in extracting the features of time series for a long-time span. The strong learning and mining ability of LSTM networks is suitable for a large quantity of time series in power transmission and distribution. SVM, with a strong generalization ability and robustness, is introduced for classification to get the final prediction results. Considering the overfitting problem in fault prediction, layer of dropout and batch normalization are added into the network. The complete network architecture is shown in this paper in detail. The parameters are adjusted to fit the specific situation of the actual power system. The data for experiments are obtained from the Wanjiang substation in the China Southern Power Grid. The real experiments prove the proposed method's improvements compared with current data mining methods. Concrete analyses of results are elaborated in this paper. A discussion of practical applications is presented to demonstrate the feasibility in real scenarios.