A data mining technique based on pattern discovery and k-nearest neighbor classifier for transient stability assessment

This paper presents a novel strategy based on pattern discovery algorithm and extended A-Nearest Neighbor (k- NN) classifier to design the power system transient stability assessment (TSA) scheme. A pattern discovery algorithm based on residual analysis and recursive partitioning is employed to identify the latent structure (called pattern) of data samples. An extended A-NN classifier is then designed to label the input sample with stability levels where the identified patterns are applied as initial classified data set. In order to improve the pattern discovery efficiency, a feature selection process is introduced in the first stage to extract kernel-feature subset from the pre-contingency steady state parameters as the input features for the TSA scheme. Applications of the proposed scheme on two IEEE test systems prove its feasibility and show good performance in stability level classifications.