Power Quality Assessment and Event Detection in Distribution Network with Wind Energy Penetration Using Stockwell Transform and Fuzzy Clustering

Power quality (PQ) is a vital issue in the present power systems integrated with large renewable energy sources since more power electronics devices are incorporated in the system. This article proposes a novel method for assessing PQ associated with wind energy integration. This method is effective to recognize PQ issues in power systems with high penetration of wind energy with a low computational burden. Furthermore, it detects different operational issues in the distribution network. Stockwell transform (S-transform) is utilized to decompose the voltage signal and calculate the S-matrix. To assess the PQ, a plot is developed from this matrix. The features of this matrix such as mean, standard deviation, and maximum deviation are further utilized for detecting the operational issues such as wind speed variation, islanding, synchronization, and outage of the wind generation by using clustering with fuzzy C-means. A modified IEEE 13-bus test system is utilized to validate the proposed method, which is also supported by hardware and real-time digital simulator results. The quality of power is graded with the help of a proposed PQ index under various operational events with different levels of wind energy penetration. The proposed method is effective for the identification and grading of different operational events in terms of PQ and recognizing a wide range of PQ issues with a high share of wind energy. The performance of the proposed scheme is established by comparing its results with other approaches.