Anomaly Detection of Solar Power Generation Systems Based on the Normalization of the Amount of Generated Electricity

Abstract

Solar power generation has attracted significant attention recently as a safe and environmentally friendly renewable energy source. However, generally speaking, since the service lives of solar power systems are relatively long, and since it is difficult to detect anomalies in individual solar panels, such plants tend to operate without much consideration for individual panel anomalies. In order to more comprehensively monitor solar power generation systems, the National Institute of Advanced Industrial Science and Technology (AIST) of Japan has developed a direct current (DC) power line communication system that enables monitoring of each panel in a system. Monitored data are then integrated and uploaded to the cloud. Using this monitored data, we found that the integrated power ratio trends of single panel power output are in accordance with a normal distribution. Therefore, herein, we propose an anomaly detection method that uses a normal distribution. We then describe an experiment using 24 solar panels into which pseudo-faults were induced and show that our proposal makes it possible to detect errors with high accuracy.