Applying machine learning on solar panel telemetry to better predict remaining useful life
Challenge
Arundo helped a client optimize the maintenance of their large solar farm. The client was struggling with the unpredictable nature of solar panel degradation and equipment failures, which led to costly and inefficient maintenance practices.
Panel issues, such as micro cracks, soiling, and hot spots, were impacting energy production and leading to cost inefficiencies for maintenance teams. Failures in supporting equipment, like inverters, were making the farm's production less predictable.
Solution
Arundo addressed these challenges by using historical data to provide a more accurate prediction of the remaining useful life (RUL) of the solar panels. This allowed the client to optimize replacement cycles and plan maintenance activities more effectively. Additionally, Arundo used historical data on soiling patterns and panel efficiency drops to enable more efficient maintenance schedules.
Furthermore, Arundo implemented equipment monitoring for inverters and other supporting infrastructure to provide timely advanced warnings of potential failures. This proactive approach allowed the client to address issues before they escalated, minimizing downtime and maximizing energy production.
Impact
By implementing Arundo’s solutions, the client was able to reduce maintenance costs significantly. The improved RUL prediction and optimized maintenance schedules allowed for more targeted interventions, minimizing unnecessary expenses. The early warnings of potential equipment failures also prevented costly repairs and downtime. Overall, Arundo’s solutions improved the solar farm's efficiency and increased its overall output. The data-driven approach to maintenance allowed the client to make informed decisions, leading to a more reliable and cost-effective operation.
Do you also need to improve your RUL predictions? Read more about our enabling technology or get in touch to discuss your particular use case.
