An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network. (August 2022)

• Record Type: Journal Article
• Authors: Korkmaz, Deniz; Acikgoz, Hakan
• Is Part Of: Engineering applications of artificial intelligence. Volume 113(2022)
• Publication Date: 2022-08
• Subjects: Solar energy -- PV modules -- Fault classification -- Convolutional neural network -- Transfer learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104959
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Photovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs. In this study, an efficient PV fault detection method is proposed to classify different types of PV module anomalies using thermographic images. The proposed method is designed as a multi-scale convolutional neural network (CNN) with three branches based on the transfer learning strategy. The convolutional branches include multi-scale kernels with levels of visual perception and utilize pre-trained knowledge of the transferred network to improve the representation capability of the network. To overcome the imbalanced class distribution of the raw dataset, the oversampling technique is performed with the offline augmentation method, and the network performance is increased. In the experiments, 11 types of PV module faults such as cracking, diode, hot spot, offline module, and other classes are utilized. The average accuracy is obtained as 97.32% for fault detection and 93.51% for 11 anomaly types. The experimental results indicate that the proposed method gives higher classification accuracy and robustness in PV panel faults and outperforms the other deep learning methods and existing studies. Highlights: The Multi-scale network efficiently classifies photovoltaic panel anomalies. Oversampling approach overcomes the imbalanced class distribution. Multi-scale branches aim to improve the features extracted by each parallel block. Proposed method is validated with a large dataset collected from six continents.