An implementation of S-transform and type-2 fuzzy kernel based support vector machine algorithm for power quality events classification

This paper aims to develop a new idea for the classification of power quality disturbances. The method is based on Stockwell's-Transform (ST) and Type-2 Fuzzy Kernel Support Vector Machine (T2FK-SVM). Through the introduction of ST and its properties, we propose a classification plan for nine types of power quality disturbances. Firstly, features of disturbance signals extracted through the ST. Secondly, features extracted by using the ST are applied as input to T2FK-SVM classifier for automatic classification of the power quality (PQ) disturbances. Design of Kernel is a main part of many kernel based methods such as Support Vector Machine (SVM), so by using of Type-2 Fuzzy sets as a kernel of SVM, the total accuracy of classification enhanced.This method can reduce the features of the disturbance signals significantly, and so less time and memory is required for classification by the T2FK-SVM method. Six single event and two complex event as well normal voltage selected as reference are considered for the classification. The simulation results showed accurate classification, fast learning and execution in the detection and classification of PQ events. Results are compared with other methods and the robustness of proposed method evaluated under noisy conditions. Finally, proposed method is also implemented on real time PQ disturbances to confirm the validity of this method in practical conditions.