Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system

In this paper, a new approach based on decision tree algorithm to detect and diagnose the faults in grid connected photovoltaic system (GCPVS) is proposed. A non-parametric model to predict the state of GCPVS by learning task is used; a data set is collected from GCPVS by the acquisition system under several weather conditions. Three numerical attributes and two targets are chosen to form the final used data, the attributes are temperature ambient, irradiation and power ratio calculated from measured and estimated power, the first target is either healthy or faulty state for detection; the second one contains four classes' labels named free fault, string fault, short circuit fault or line-line fault for diagnosis. The Sandia model is applied to estimate the power generated from GCPVS operating in healthy state. The data set has been divided into two parts, where 66% was used for the learning and the remained for testing. Subsequently, a new data was recorded from five days in order to evaluate robustness, effectiveness and efficiency of both models. Testing result indicate that the models have a high prediction performance in the detection with high accuracy while the diagnosis model have accuracy equal to 99.80%. Moreover, the models have been evaluated in five days; the added data guarantees the prediction efficiency resulting in high accuracy for the detection and the diagnosis, whereas the classification is correct for 99%.