AI AND DATA ANALYTICS FOR PREDICTIVE MAINTENANCE IN SOLAR POWER PLANTS

Abstract

Predictive maintenance in solar power plants is increasingly recognized as a crucial strategy for enhancing operational efficiency and minimizing downtime. This approach leverages artificial intelligence (AI) and data analytics to analyze vast amounts of data generated from various components of solar installations, including inverters, panels, and battery systems. By employing advanced algorithms and machine learning techniques, predictive maintenance enables the early detection of potential failures and performance degradation, allowing for timely interventions and repairs. The integration of AI enhances the traditional maintenance practices by utilizing real-time data from sensors, historical performance records, and environmental factors. Data analytics provides actionable insights that optimize maintenance schedules, reduce operational costs, and extend the lifespan of equipment. Moreover, the application of predictive analytics helps in forecasting energy production and consumption patterns, enabling better resource allocation and planning. As the renewable energy sector continues to grow, the adoption of AI and data analytics in predictive maintenance will play a vital role in improving the reliability and sustainability of solar power plants. This paper explores the methodologies and technologies involved in implementing predictive maintenance strategies, highlighting case studies that demonstrate the effectiveness of these innovations. The findings suggest that a proactive maintenance framework not only enhances the operational efficiency of solar facilities but also contributes to the overall advancement of renewable energy technologies.