Advanced Diagnostic Approach of Failures for Grid-Connected Photovoltaic (PV) Systems

Abstract

Real-time identification of failures in photovoltaic (PV) systems is crucial for achieving reactive maintenance schemes that, in turn, will increase the system reliability and guarantee the lifetime output. Following this line, failure detection routines (FDRs) that operate on acquired data-sets of grid-connected PV systems were developed to diagnose the occurrence of failures. The developed FDRs comprise of a failure detection and a classification stage. The detection stage was based on the comparison between the measured and predicted DC power production against set threshold levels (TL). The classification stage was based on data-driven algorithms, which were used to post-process the detected failure patterns through the application of the developed decision trees (DT), k-nearest neighbours (k-NN), support vector machine (SVM) and fuzzy inference systems (FIS). The experimental results showed that the FDRs were capable of detecting all the different types of failures (open-and short-circuited PV module, inverter shutdown, shorted bypass diode and partial shading) that were introduced to the test-bench PV system. Finally, amongst the investigated models, the k-NN model achieved the highest average true negative rate of 91%, when classifying each type of failure used for benchmarking.