New automated power quality recognition system for online/offline monitoring

• We improve monitoring problems such as execution time, complexity and accuracy.
• We provide fast and effective structures for online/offline PQDs classification.
• We suggest a comprehensive study to extract and select features vector.
• Various and large number of extracted features are remarkably severable.
• The minimum features are selected to reflect characteristics signals, precisely.

One of the most important issues in the PQ assessment is diagnosis of Power Quality Disturbances (PQDs) using an effectual low computational burden strategy. We recommend a new approach for PQ analysis that addresses several major problems of prior works, including algorithm execution time, computational complexity, and accuracy. This paper suggests an effective and comprehensive method, so-called “integrated approach”, for extracting features using integration of discrete wavelet transform and hyperbolic S transform. Moreover, a comparative assessment of PQDs recognition using various combinations of different Feature Selection (FS) and classification methods is presented. FS can reduce the dimension of feature space which leads to better performance of detection system. Four well-known FS techniques namely modified relief, mutual information, sequential forward selection, sequential backward selection and three benchmark classifiers, are considered. The particle swarm optimization is used to obtain optimal parameters of these classifiers. The key attribute of this paper is that it yields good time–frequency resolution with low computation burden for optimal PQ monitoring structure. Empirical results show that the proposed structures can yield an automatic online/offline monitoring of PQ with sparser structures and less computational execution time, both in the training and recognition phases, without sacrificing generality of performance.