Intelligent Power Quality Event Recognition Through MRA-DWT in Combination with AdaBoost Classifier

The reliable and efficient operation of electrical power systems is paramount in modern society, necessitating the continuous monitoring and timely recognition of power quality disturbances. This paper presents an intelligent approach for Power Quality Event Recognition (PQER) by combining Multiresolution Analysis with Discrete Wavelet Transform (MRA-DWT) and the AdaBoost classifier. The MRA-DWT technique allows for the decomposition of power signals into multiple scales, enabling the extraction of informative features from both time and frequency domains. Subsequently, the AdaBoost classifier is employed to enhance the classification accuracy by training an ensemble of weak learners, thereby creating a robust and adaptive PQER system. The proposed methodology is evaluated on a real-world dataset of power quality disturbances, showcasing its effectiveness in distinguishing and classifying various types of disturbances, including voltage sags, swells, interruptions, harmonics, and transients. Experimental results demonstrate the superiority of the MRA-DWT-AdaBoost combination over traditional methods, achieving higher accuracy and faster recognition of power quality events. This research contributes to the advancement of intelligent power quality monitoring systems, offering a promising solution for enhancing the reliability and stability of electrical grids through the timely identification and classification of power quality disturbances.