Power quality disturbance classification using a statistical and wavelet-based Hidden Markov Model with Dempster–Shafer algorithm

A novel approach for power quality disturbance classification using Hidden Markov Model (HMM) and Wavelet Transform (WT) is proposed in this paper. The energy distributions of the signals are obtained by wavelet transform at each decomposition level which are then used for training HMM. The statistical parameters of the extracted disturbance features are used to initialize the HMM training matrices which maximize the classification accuracy. Fifteen different types of power quality disturbances are considered for training and evaluating the proposed method. The Dempster–Shafer algorithm is also used for improving the accuracy of classification. In addition, the effect of the noise is studied and the performance of a denoising method is also investigated. Simulation results in a 34-bus distribution system verify the performance and reliability of the proposed approach. Also the results obtained for practical data prove the capability of the proposed method for implementing in experimental systems.

► A PQ disturbance classification approach based on WT and HMM is proposed. ► Fifteen different events are classified. ► Dempster–Shafer algorithm for final decision is used.