Smart Detection and Mitigation of Power Quality Issues in Smart Grids Using MATLAB-Based Simulation and Optimization

Abstract

Power quality issues are becoming more prevalent in investigation of smart grids due to the integration of various distributed energy resources (DER), therefore this comprehensive study provides a smart detection and mitigation approach based on MATLAB-based smart grid simulation and optimization approaches. Power quality disturbance including voltage sags, harmonics, and transients is one of the considerable problems for the reliable operation of the grid, and its appropriate management is significant to keep the system stable and efficient. Methodology combines real-time monitoring systems to advanced signal processing algorithms used for detection of different types of power quality disturbances. We employ a suite of optimization methods which are used with fuzzy logic control and particle swarm optimization (PSO) to devise mitigation strategies to minimize the effects of these disturbances on the system. MATLAB is used for the implementation of the simulation, and finally the developed algorithms are examined for different grid models and disturbances. The experimental results show that the proposed detection system is able to accurately identify disturbances in both transient and non-transient conditions with high speed, while the optimization-based mitigation strategies lead to a considerable reduction in the severity of power quality disturbances, subsequently enhancing grid behavior overall. The paper also elaborates on the scalability of the offered framework, arguing it would be deployable across large smart grid networks. This work utilizes simulation-based tools and intelligent optimization techniques to advance smart grid reliability and resilience through a robust solution for a modern electrical grid with increasing complexity and demand.