Power Quality Optimization using Deep Learning

Power quality optimization is increasingly important in modern electrical systems due to the rising complexity of power grids and the integration of renewable energy sources. This paper introduces a novel approach to power quality management using Deep Learning techniques, specifically Deep Neural Networks (DNNs), to predict and mitigate power disturbances such as harmonics, voltage sags, and frequency variations in real time. By incorporating Active Power Filters (APFs) and Proportional-Integral (PI) controllers, the system dynamically adjusts voltage levels and reactive power compensation to optimize power quality. Real-time data collection from smart sensors enables the DNN to analyze and predict potential issues before they impact the system, ensuring a proactive approach to power management.