Pattern Recognition of Time-Varying Signals Using Ensemble Classifiers

摘要

A new classification approach for time-varying power quality (PQ) signals using ensemble classifiers (EC) is proposed in this paper. To achieve high performance, existing expert systems require several signal features so that these systems have more computational complexity. In order to reduce the computational cost and to improve the accuracy further, a new set of features called moments and cumulants are introduced in this paper to classify PQ events. Further, the performance of various ensemble classifiers is analyzed with the proposed feature set. Moreover, the analysis is carried out with different training and testing rates. Finally, the performance comparison is made with that of the existing techniques to prove the superiority of the proposed features and classifiers.