AI-Based Monitoring and Control of Grid-Connected Solar Systems

Abstract

The integration of renewable energy systems into existing power grids is critical for sustainable energy production. Among these, solar energy systems are gaining prominence due to their environmental benefits and scalability. However, optimizing their performance in real-time is a complex challenge, particularly when connected to large-scale grids. This study proposes an AI-based monitoring and control system for grid-connected solar systems to enhance operational efficiency, reliability, and predictability. The system utilizes machine learning algorithms, such as neural networks and reinforcement learning, to predict solar energy generation based on weather conditions, historical data, and real-time inputs. These algorithms are designed to adapt to changing environmental factors, identify potential faults, and provide proactive maintenance recommendations. The system's ability to dynamically adjust the operation of the solar panels and the grid connection ensures optimal energy flow, reducing energy losses and maximizing system efficiency. The proposed model aims to offer scalable solutions for integrating solar energy into larger grids, supporting the transition to greener, more sustainable energy infrastructures. Through the implementation of this AI-based framework, the study demonstrates a significant improvement in system performance, fault detection, and energy optimization, thus contributing to more efficient grid-connected solar power systems.