A Novel Method of Power Quality Disturbances Measures Using Discrete Orthogonal S Transform (DOST) with Wavelet SupportVector Machine (WSVM) Classifier

摘要

This study proposes a novel method based on Discrete Orthogonal S-Transform (DOST) and Wavelet Support Vector Machines (WSVM) for detection and classification of power quality disturbances. DOS-transform is mainly used to extract features of power quality disturbances and support vector machines are mainly used to construct a multi-class classifier, which can classify power quality disturbances according to the extracted features. Results of simulation and analysis demonstrate that the proposed method can achieve higher correct identification rate, better convergence property and less training time compared with the method based on Probabilistic Neural Network (PNN). Therefore, through this method power quality disturbances can be detected and classified effectively, accurately and reliably.