Two-stage Transient Stability Prediction Method of Power System Considering Cost of Misdetection and False Alarm

There exist misdetection (misclassification of unstable samples into stable samples) and false alarm (misclassification of stable samples into unstable samples) by the transient stability prediction method based on artificial intelligence, which is a major obstacle to practical engineering applications. In response to this deficiency, this paper proposes a two-stage power system transient stability prediction method based on the convolutional neural network (CNN) considering the cost of misdetection and false alarm. At the first stage, the corresponding sliding time window input features are trained to obtain different layers of integrated CNN models, and the credibility index for each output layer is established. Then, the credibility threshold optimization selection problem is transformed into a multi-objective optimization problem. This stage could minimize or even eliminate the misdetection and output credible samples with high credibility as soon as possible. At the second stage, an emergency control start-up strategy based on multi-criteria fusion for the credible unstable samples predicted at the hierarchical prediction stage is proposed to reduce the actual loss caused by false alarm. The analysis of a simulation system shows that the proposed method can minimize or even eliminate the misdetection at the minimum cost, and promote the probability of practical engineering application of transient stability prediction results based on artificial intelligence method. © 2020 Automation of Electric Power Systems Press.