A Novel Method of Characterization Classification and Detection of Voltage Sags

Abstract

In the active landscape of power systems, the quality of electrical energy is of paramount importance. Among various power quality issues, voltage sags represent a significant challenge, influencing not only the efficiency of power distribution but also the operational reliability of end-user equipment. This thesis introduces a novel approach to the characterization, classification, and detection of voltage sags, aiming to enhance the stability and reliability of power systems. The phenomenon of voltage sags, often resulting from fault conditions, equipment operations, or sudden large load variations, can lead to severe economic losses and operational disruptions in sensitive industrial processes. Traditional methods of addressing these sags have revolved around monitoring and mitigation strategies. However, with the increasing complexity of power networks and the integration of renewable energy sources, there is a pressing need for more advanced and adaptive techniques. This research proposes a comprehensive method that not only identifies and categorizes voltage newlinesags but also detects them with high accuracy and speed. The method is grounded in new newlineprocessing techniques and leverages the latest advancements in the technology. This dual newlineapproach allows for a deeper understanding of the underlying causes of voltage sags, facilitating more targeted and efficient mitigation strategies. First part of this research focuses on developing of a new technique for the classification and characterization of single-phase (1-and#934;) voltage sag, based on the type of grounding and fault. It is shown that the characteristics of 1-and#934; voltage sag are greatly dependent on the nature of the grounding and type of fault. New indices have been introduced which result in 5 classifications of single-phase voltage sags. Before delving into analysis of voltage sags, modelling of voltage sags is necessary. Therefore, various methods for modelling voltage sags have been developed. Second part of this research focusses on developing novel techniqu