SMART SYSTEM FOR PREDICTIVE MAINTENANCE OF SOLAR PV (PHOTOVOLTAIC) SYSTEMS

The widespread adoption of solar photovoltaic (PV) systems as a sustainable energy source has underscored the need for effective maintenance strategies to ensure long-term reliability and performance. Traditional maintenance approaches, such as corrective and preventive methods, often resulted in unplanned downtime or unnecessary servicing, leading to increased costs and energy losses. This project developed a smart system for predictive maintenance of solar PV systems, employing machine learning (ML) to enhance fault detection and optimize maintenance schedules. The methodology adopted the Structured Systems Analysis and Design Method (SSADM) and Object-Oriented Analysis and Design Method (OOADM). The system integrated historical data, stored on local device storage, and processed it using Long Short-Term Memory (LSTM) neural networks implemented in Python to predict faults. A graphical user interface (GUI) was also built using Python’s Tkinter, enabling users to input data and run predictions with the model. The approach included requirements analysis, system design, data collection, and data analysis, prioritizing scalability, reliability, and cost-effectiveness. The system minimized downtime, reduced maintenance costs, and improved energy yield by providing timely maintenance alerts through the user-friendly GUI. Challenges, such as limited real-time data and variability in PV system configurations, were addressed by leveraging historical data during the implementation phase. The developed predictive maintenance framework achieved a fault prediction accuracy of at least 80%, contributing to advancements in data-driven renewable energy management.