Disturbance classification using Hidden Markov Models and Vector Quantization

This paper presents a novel approach to the classification of power quality disturbances by the employment of Hidden Markov Models. In these models, power quality disturbances are represented by a sequence of consecutive frames. Both the Fourier anal Wavelet Transforms are utilized to produce sequence of spectral vectors that can accurately capture the salient characteristics of each disturbance. Vector Quantization is used to assign chain of labels for power quality disturbances utilizing their spectral vectors. From these labels, a separate Hidden Markov Model is developed for each class of the power quality disturbances in the training phase. During the testing stage, the unrecognized disturbance sequence is matched against all the developed Hidden Markov Models. The best-matched model pinpoints the class of the unknown disturbance. Simulation results prove the competence of the proposed algorithm.