Power Quality Events Recognition Using S-Transform and Wild Goat Optimization-Based Extreme Learning Machine

This paper presents a novel approach for automatic power quality (PQ) event detection and classification based on Stockwell transform (S-transform) and wild goat optimization (WGO)-tuned extreme learning machine (ELM). The distinctive features associated with PQ event signals have been extracted by S-transform to obtain the feature vectors characterizing the signal nature. Considering these feature vectors as input, a classifier based on ELM optimally tuned with modified WGO technique is proposed. The WGO technique originated from the social hierarchy and strategic planning to reach at peak by the wild goats in nature is adapted to formulate an effective ELM model by parameter tuning for better classification. To justify the enhanced performance of the proposed approach, it is tested on a wide range of extracted synthetic PQ event data by MATLAB simulation. To ensure the real-time implementation, the PQ event data with the addition of 20, 30, and 50 dB to the synthetic signals are considered. The analysis of results presented reveals a very high performance for both PQ event recognition and classification, ensuring the efficiency of the proposed approach.