Power quality disturbance feature extraction and recognition

Poor quality of power supplies could have the potential to interfere with communication networks, increase power losses, reduce life periods of electrical/electronic equipment, and cause a variety of malfunctions in power generation, transmission, distribution, and in end-users' systems. Therefore, it is imperative to ascertain what power quality (PQ) problems the electricity grids are currently suffering and what are the formats and occurring frequencies of them, and then find out necessary countermeasures to mitigate the impacts they have been bringing about. Apparently, techniques of effective feature extraction and accurate classification are essential for the PQ disturbance recognition required by a smart grid. In the paper, after comparing some main feature extraction approaches, the authors present a PQ disturbance recognition scheme based on the combination of support vector machines and error correcting output codes. With the proposed feature extraction using Fourier and wavelet transforms respectively, the performance of the designed recognition system is verified. Simulations have shown that the proposed recognition methods, in particular when using the Fourier transform, can achieve superior performance in terms of simplicity of feature extraction and high accuracy of the classification.