Fault Diagnosis in Photovoltaic Arrays Using GBSSL Method and Proposing a Fault Correction System

Nonlinear characteristics of solar cells and changes in environmental conditions, such as temperature, and in particular, the intensity of daytime irradiation, make it difficult to identify faults by the conventional means of protection. Therefore, a variety of machine learning techniques are proposed for fault detection in photovoltaic (PV) arrays. In this regard, classifying and identifying the location of a fault event is essential. In addition to fault recognition, selecting the method of fault correction is another issue to be addressed. However, there are scarce investigations in this field. In this paper, a comprehensive method for identifying, classifying, locating, and correcting faults is introduced. The proposed method is assessed with the expansion of the diagnostic space of the graph-based semisupervised learning algorithm and an increased number of class labels. After identifying the type and location of a fault, the system temporarily isolates the fault to function without interruption until it is fully corrected. The problem of overlapping cell data in normal and fault-prone modes is resolved by applying different methods of normalization. The results show that all faults including unlearned and learned in a wide range of environmental conditions, where possible PV arrays are experienced, are properly identified and corrected. Moreover, our studies demonstrate that the proposed system mitigates the output voltage variations over a fault-prone mode.