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A Dilation and Erosion-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Due to the incremental economic and environmental crisis, solar energy is becoming one of the most promising supplements for electric power generation. In order to eliminate the risks of safety hazards in the Photovoltaic system, fault detection technology is a priority research topic. Since the data distributions of different operation conditions present nonspherical clustering features, various supervised and semi-supervised intelligent learning methods have been widely carried out to detect and classify various fault conditions. However, the number of fault types needs to be determined in advance in these methods. This step takes a lot of time in the process of fault identification. In this paper, an original clustering method based on dilation and erosion theory is proposed. The main advantage of this approach is that the clustering number does not need to be predetermined. The simulation and experimental results verify that the proposed method has the high adaptability and validity under a wide range of meteorological conditions.