Data-driven subspace-based adaptive fault detection for solar power generation systems

Data-driven fault detection has emerged as one of the most prevalent topics in the fault diagnosis. In this study, a novel data-driven subspace-based fault-detection scheme is proposed to handle the problem of fault detection with system uncertainties in solar power generation systems. A data-driven subspace-based predictor is developed by using the input-output measurements. The residual signal is generated from the predictive error of the predictor and a fault-detection filter that is designed to diminish the influence of system uncertainties. An adaptive algorithm is developed for updating the fault-detection filter. Faults can be detected by comparing the evaluated residual signal with a threshold. The reliability of the designed fault-detection scheme is verified in three cases in a solar power generation system.