Smart Control for Solar PV-Grids to Enhance Power Quality using Deep Neuro Auto Inverter Model

Power quality upgrade in grid-connected photovoltaic systems ensures stable and highly efficient operation of modern energy grids. Meanwhile, the high penetration of renewable energy sources brings real challenges to the maintenance of high power quality, associated with the problems of harmonics, voltage instability, and reactive power balance. In this paper, an advanced neural network-based control for the inverter is utilized to dynamically optimize inverter settings for the abatement of common power quality problems. It will adapt the model through deep learning algorithms to real-time conditions—a sophisticated approach in managing harmonic distortions, stabilizing voltage fluctuations, and optimizing reactive power. The proposed NAIC model represents a significant improvement over the traditional methods of control. Its flexibility and adaptability enhance efficiencies in power conversion and thus improve the stability of the grid. The NAIC learns from real-time data, adapting control parameters continually during the process, which enhances the performance in solar power systems for assured reliability and efficiency of operation of the power grid. This paper presents effectiveness of neural network approaches for power quality management, and describes future directions towards more research and developments in this crucial area of renewable energy integration.