Power quality disturbances categorization using Identity Feature Vector and Extreme Learning Machine

Abstract

Power quality disturbances are variations or anomalies in the voltage, current, or frequency of electrical power that can affect the proper operation of electrical equipment. These disturbances are usually classified into different categories based on their attributes and effects. This article presents an intelligent technique based on an Identity Feature Vector and an Extreme Learning Machine (ELM). This study first derives a constant length vector for each disturbance signal. A wavelet transform is applied to derive attributes from the input disturbance signal, and the identity vector is formed using the approximation coefficients. After the required normalization procedures, the normalized identity vector is classified using an ELM. To assess the productivity of the suggested approach, 12 types of disturbances, single and combined, are generated, and the system's efficiency is studied. The results indicate that ten out of 12 combinations, including Harmonic, Sag, and Flicker, were detected with 100 % accuracy. Additionally, the combination "Harmonic + Swell" exhibited the lowest accuracy, identified with 98 % accuracy. The total average accuracy of this method is 99.75 %. The outcomes demonstrate the highly favorable performance of this approach. This study evaluated the analyzed algorithm under noisy conditions with three different noise levels: 30 dB, 40 dB, and 50 dB, respectively. The average prediction accuracy for these three noise levels is 99.16 %, 99.25 %, and 98.91 %. The outcomes demonstrate that the evaluated algorithm accurately detects power quality disturbances across various noisy conditions.