Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

Abstract

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.

Condition Assessment of Medium Voltage Assets: A Review

“…Moreover, Figure 2 demonstrates an apparent increasing interest on the topic in the last few years. Therefore, emphasis is placed on the analysis of recent literature reviews [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. In addition, the literature is complemented with standards [69][70][71], selected papers [72][73][74][75][76], and reports [77].…”

“…The work in [53,63] extend the overview of electrical faults on the PV array, inverters, and the AC side of PV systems. In addition, [54,66] analyze not only electrical faults, but also physical, e.g., damage, cracks, and deterioration in PV panels, and environmental faults, e.g., temporary, and permanent partial shading. As an alternative to detailed modeling and simulation, PV and solar emulators are also available to perform a variety of tests [73,74].…”

“…Approaches based on AI techniques have been extensively used in the last few years. For instance, machine learning tools such as SVM, ANN, Generative Adversarial Networks (GAN), transfer learning, and decision trees are used for automatic analysis of images in infrared thermography [56,62,64,69], flash, electroluminescence, and X-ray [59,60,72], and in fault diagnosis applications [52][53][54]56,58,61,63,66,73,74]. Similarly, deep learning tools such as Deep Belief Networks (DBF), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), have been also applied recently because of their extended capabilities for image analysis.…”

Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems

“…A back-propagation algorithm, which represents a kind of steepest descent technique, is utilized to learn an MNN by adjusting the weights. The main function that was used to train the NN is depicted in detail in [44,45].…”

Research on photovoltaic system faults detection strategy based on memory network

“…This process is important for ensuring the system is operating at its optimal level and that any potential problems are identified and addressed quickly [6] . Fault detection can be done through visual inspection, electrical testing, and data analysis [7] . By using these methods, any faults in the system can be identified and addressed before they become a major issue.…”