Power system transient stability assessment based on cluster features of rotor angle trajectories

Abstract

Multiple machine learning techniques have been widely used in transient stability analysis. For machine learning based method, balance between input feature number and total calculation efficiency is always a problem need to solve. In this paper, a hybrid classifier combining linear support vector machine (LSVM) and decision tree (DT)was proposed to assess transient stability using rotor angle trajectory cluster features. Firstly, rotor angle cluster features were used as inputs. Considering time dimension of input features, each time series feature was reduced with LSVM. Then the reduced data were put into DT to generate transient stability prediction and stability degree evaluation models. Boosting technique was used to improve accuracy of the evaluation model. Case studies were conducted on New England 10-machine 39-bus system to verify the proposed method. Test results showed that the proposed cluster features and algorithm possesses high accuracy and overall calculation efficiency. The evaluation model could indicate stability degree accurately and was robust to untrained samples.