Solar inverter anomaly detection is critical for maintaining the efficiency and reliability of solar
power systems, where the large number of inverters complicates maintenance schedules and reliability estimations. Prioritizing and planning maintenance for these critical components poses significant challenges, particularly in terms of evaluating and ranking the health of each inverter.
A sophisticated solution lies in predicting the health score of inverters based on ambient
conditions and power generation parameters. By leveraging advanced data analytics and machine learning techniques, operators can identify complex multivariate patterns that serve as early indicators of potential faults or anomalies. This predictive approach enables proactive maintenance and the efficient allocation of resources, ensuring that maintenance efforts are strategically directed toward inverters exhibiting signs of degradation or failure.