Photovoltaic Failure Diagnosis using Sequential Probabilistic Neural Network Model

With increasing the installation of the photovoltaic modules, the different failures on components of the system are dramatically increased and thus lead to a direct impact on system productivity and efficiency. Fault detection and diagnosis accurately have an extreme impact on the maintenance and reliability of photovoltaic array. Moreover, for detecting the different faults, selecting the proper indicators for monitoring the system improves the fault diagnosis techniques performance and avoids the complexity of the system. In this paper, an efficient detection and diagnosis model for different fault types is proposed. This model has sequential steps. Firstly, the performance of seven indicators is initially analyzed to predict accurately the nonlinear output behavior of the photovoltaic system under changing environmental conditions, hence select the minimum indicators to detect the typical faults. Secondly, ten fault cases, considering single-fault types and another three faults considering multi-fault types, are investigated. At the same time, the impact of these faults on the selected indicators is deeply analyzed. Finally, the typical fault types are classified and detected effectively depending on three sequential probabilistic neural network models, which give a precise classification of the data inputs. Both theoretical and experimental tests are operated to validate the performance and effectiveness of the proposed model.