Condition classification and performance of mismatched photovoltaic arrays via a pre-filtered Elman neural network decision making tool

Photovoltaic array systems operated in harsh and versatile environments generate complex nonlinear characteristics due to various mismatched phenomena, which undermines the system performance unprecedentedly and challenges the existing monitoring technologies. To this end, a class of modern data acquisition and smart condition monitoring systems are introduced with the advanced technologies such as sensor networks, smart combiner boxes and smart inverters. Furthermore, a novel condition monitoring approach for identifying and classifying mismatched photovoltaic arrays is proposed that consists of pre-filter, Elman neural network and decision-making rules to deal with various mismatches such as partial shading due to tree leaves or dirt dropping, and open circuit faults. Novel micro solar power stations are built and used to prove the concept via a comparable experimental study that has never done before. Experimental results show that applying pre-filters on time series input attributes improves the performance up to 3.70% and modifying the decision making formulae improves further the performance up to 1.78%. Eventually, the entire network could identify all of fault conditions if the parameters are chosen properly. Therefore, the proposed method is promising in solar power systems and the constructed apparatuses are useful in the study of modern photovoltaic power systems.