Developments of AI-Assisted Fault Detection and Failure Mode Diagnosis for Operation and Maintenance of Photovoltaic Power Stations in Taiwan

Abstract

Fault detection and failure mode diagnosis are of crucial importance in operation and maintenance (O&M) of photovoltaic (PV) power stations. In this work, advanced artificial intelligence techniques are exploited to optimize these O&M tasks for 150 PV power stations in Taiwan with total power rating around 54 MW. First, the response of each inverter under the maximal power tracking is monitored and analyzed by machine learning (ML) algorithms in every five minutes. The alert of fault detection will be activated if the power output of each inverter is significantly different from its nominal output. Prompt notification will be sent to user by mobile devices or emails immediately. To further enhance the performance of power prediction for multiple oriented roof-top PV systems, the power prediction model will be upgraded by simulated plane of array (POA) irradiance instead of direct measurements from only one pyranometer. Two-year field test results from 74 PV power stations with 4,792 inverters indeed demonstrate the effectiveness of the proposed AI-based O&M schemes for PV power stations.