Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with fault diagnosis of solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed. After reviewing relevant work, a monitoring tool is designed using thermography and artificial intelligent systems that allows the detection of various types of faults in PV modules and at the same time the designed tool aims to filter the nonsignificant anomalies. A neural network (NN) classifier is applied to the transfer characteristics (I‐V data) of the faulty PV module for the diagnosis which adapts multilayer perceptron (MLP) networks to identify the type and location of occurring faults. The Discrete wavelet transform (DWT) based signal processing technique is utilized in the feature extraction process to reduce the NN input size. The developed detection algorithm is adapted for 24/7 automated surveillance. For a given fault condition, the average fault detection time is observed to be <9 seconds, which is lower than the previous work done. The developed algorithm achieved 100% accuracy when tested on a predetermined fault data set.

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.

This paper presents a dynamic voltage support (DVS) scheme for achieving low voltage ride-through (LVRT) with a grid connected photovoltaic (PV) inverter during the voltage sag fault. The DVS scheme is achieved by formulating an additional reactive active current control (ARACC) mode which is developed from a conventional reactive current control (RCC) approach. This provides stable operation of the system and achieves higher effectiveness due to the lower X/R ratio at the point of common coupling (PCC) in low voltage (LV) networks. Further, the performance of the proposed controller is assessed by simulating a 4 kW system for voltage sag faults. Further, the proposed controller tested with the real-time simulations and experimental setup for voltage sag conditions. The results presented demonstrate that the active and reactive power is regulated in concordance with grid code requirements. The controller achieves LVRT within the time limits of grid standard during symmetrical faults, which makes it appropriate for fast transient events. This operates the PV system under its nominal capacity, avoiding unwanted grid disconnection events.

Fault classification and detection are necessary for safety, efficiency and reliability of photovoltaic systems. Regardless of the fact that PV system requires low maintenance due to the absence of moving parts, they are still vulnerable to many faults. Particularly for PV modules, it's problematic to shut them down completely during faults as it will affect their performance and result in early degradation. Additionally, the inability of conventional fault detection methods in early detection of faults leads to higher risk of failure. In this paper thermography and machine learning based PV module fault classification is developed. Texture feature analysis is adapted to study the features of different faulty panel thermal images. The extracted features are trained by implementing Artificial Neural network classifier to develop the fault classifier. The developed algorithm depicted 93.4% training efficiency and 91.7% testing efficiency which is better when compared with the conventional classification techniques.