Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree

TLDR

The paper addresses the recognition of single‑stage and multiple power‑quality disturbances. It proposes an algorithm that combines Stockwell's transform, an artificial neural network classifier, and a rule‑based decision tree to identify PQ disturbances. The method extracts S‑transform features from a database of IEEE‑1159‑based PQ events and feeds them into a hybrid ANN–decision‑tree classifier, tested on both simulated single‑stage disturbances (sag, swell, etc.) and multiple‑disturbance scenarios. The hybrid classifier achieves satisfactory recognition accuracy and is validated on real‑time laboratory PQ events.

Abstract

This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.