Detection and Classification of Changes in Voltage Magnitude During Various Power Quality Disturbances

Abstract

Power quality refers to the characteristics of the electrical power supply that affect the performance, reliability, and safety of electrical equipment. With the growing demand for reliable and efficient power supply, power quality has become an important area of research and development. The detection and classification of power quality disturbances through discrete wavelet transform (DWT) and machine learning is a promising approach that can improve the accuracy and efficiency of power quality analysis. DWT is a powerful signal processing technique that can decompose complex signals into different frequency bands, allowing for the identification of various types of power quality disturbances, such as voltage sags, swells, and interruptions. Supervised machine learning algorithms such as Decision Tree, SVM, KNN and Adaboost, can then be used to classify these disturbances based on their features extracted from the DWT coefficients. This paper detects and classify PQD’s using DWT and machine learning and discusses the advantages and limitations of this approach. It also provides insights into the future research directions in this area, such as the development of more sophisticated machine learning models and the integration of real-time monitoring and control systems. Overall, this paper highlights the potential of using DWT and machine learning for power quality analysis and its relevance to the development of smart grid technologies.