Current AI Trends in PV Plant Monitoring

Artificial intelligence (AI) is transforming the monitoring and management of solar photovoltaic (PV) plants, enhancing efficiency, accuracy, and strategic decision-making. As the demand for electricity continues to grow, with projections showing a significant rise in solar energy's share of the U.S. energy mix by 2035, the need for advanced AI-enhanced tools in the renewable energy sector is becoming critical. AI applications in PV plant monitoring help optimize asset performance, reduce operational costs, and improve the overall return on investment (ROI) for plant operators.

Key AI-driven innovations in the industry include the use of infrared (IR) drones for automated inspections, which drastically reduce the time and labour traditionally required for identifying faulty modules and underperforming equipment. AI-powered software can now analyse thousands of IR images in minutes, geo-locating defective modules and generating actionable insights to improve plant performance. This rapid assessment allows for quicker interventions, preventing prolonged energy loss and minimizing costly downtime. An example of this application is the Smart PV Inspection Tool developed by Enertis Applus+ to accelerate defect identification processes and increase accuracy.

In addition, AI-enhanced supervisory control and data acquisition (SCADA) systems have evolved from providing simple monthly reports to delivering real-time, granular data on system performance. These "smart" SCADA systems not only identify issues such as production losses and potential equipment failures but also offer predictive maintenance recommendations. By utilizing machine learning (ML), the software can analyse data at the component level and generate insights on factors such as module degradation, utility curtailment, and soiling. The integration of these tools enables operators to make more informed decisions and optimize the performance of each asset. An example of this application is the Enertis Applus+ Advanced Performance Analytics Application (A-PAA).

AI also plays a crucial role in energy forecasting and demand response. Advanced models predict the output of PV plants based on weather data, helping utilities and plant owners plan operations more effectively. AI can also predict grid demand, allowing operators to adjust the energy supply, manage oversaturation, and prepare for fluctuations in demand.

Furthermore, AI models are being applied in performance analytics and module defect detection. By analysing real-time data from sensors and electroluminescence (EL) tests, AI can detect anomalies in solar panels and predict how they will affect performance. These insights are valuable for optimizing maintenance schedules and ensuring that PV plants operate at peak efficiency.

In summary, AI is becoming an indispensable tool in the PV industry, helping streamline operations, enhance predictive maintenance, and drive the adoption of solar energy. As AI applications continue to evolve, their role in improving plant efficiency and ensuring a smooth transition to renewable energy will become increasingly critical in meeting the growing global demand for clean power. Looking ahead, research on Large Language Models (LLM) and other AI technologies is expected to further shape the future of the solar industry, making it an exciting time for advancements in clean energy.