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This paper proposes an Intelligent Monitoring System (IMS) for Photovoltaic (PV) systems using affordable and cost-efficient hardware and also lightweight software that is capable of being easily implemented in different locations and having the capability to be installed in different types of PV power plants. IMS uses the Internet of Things (IoT) platform for handling data as well as Interoperability and Communication among the devices and components in the IMS. Moreover, IMS includes a personal cloud server for computing and storing the acquired data of PV systems. The IMS also consists of a web monitor system via some open-source and lightweight software that displays the information to multiple users. The IMS uses deep ensemble models for fault detection and power prediction in PV systems. A remarkable ability of the IMS is the prediction of the output power of the PV system to increase energy yield and identify malfunctions in PV plants. To this end, a long short-term memory (LSTM) ensemble neural network is developed to predict the output power of PV systems under different environmental conditions. On the other hand, the IMS uses machine learning-based models to detect numerous faults in PV systems. The fault diagnostic of IMS is based on the following stages. Firstly, major features are elicited through an analysis of Current–Voltage (I–V) characteristic curve under different faulty and normal events. Second, an ensemble learning model including Naive Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) is used for detecting and classifying fault events. To enhance the performance in the process of fault detection, a feature selection algorithm is also applied. A PV system has been designed and implemented for testing and validating the IMS under real conditions. IMS is an interoperable, scalable, and replicable solution for holistic monitoring of PV plant from data acquisition, storing, pre-and post-processing to malfunction and failure diagnosis, performance and energy yield assessment, and output power prediction. [ABSTRACT FROM AUTHOR]

In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method. [ABSTRACT FROM AUTHOR]

• aIRT equipment equipped with a RGB camera improves inspection. • Post-flight image analysis provides a faster aIRT inspection. • The most common problem detected were hot spots caused by soiling and vegetation. • The most common defect in the power plants were disconnected cell substrings. • The defect with higher impact on the PV plant performance are disconnected strings. The uptime of utility-scale solar photovoltaic (PV) power plants is of utmost importance for meeting contractual energy yields and expected capacity factors. Aerial Infrared Thermography (aIRT) is a non-destructive, no-downtime, fast and cost-effective method for monitoring large-scale PV power plants and assisting in fault detection. The use of aIRT techniques aims at increasing the quality and service life of PV plants especially in sunny and developing countries such as Brazil, where there is a shortfall of specialised workforce and the costs for a detailed supervisory system of utility-scale PV power plants are high. This paper presents an analysis of an aIRT flight campaign over four utility-scale PV plants in the northeast of Brazil. Two types of measurement equipment have been tested and compared, resulting in more stability and efficiency using a commercially available solution. This solution was also equipped with a RGB camera that accelerated the inspections, since it helped to differentiate defects from hot spots caused by soiling and vegetation over the modules, which were common. Different methods for fault detection were also tested and it was concluded that post-flight image analysis provides faster results. The most common faults that can happen in the early operation of PV power plants and how operators should address and prevent them were also discussed. The most common defect detected during the campaign were disconnected cell substrings. However, disconnected strings had the most impact on the power plants energy performance. [ABSTRACT FROM AUTHOR]

• Development of a fault detection algorithm on aIRT images highlighting its main challenges, shortcomes, and workarounds; • Utilization of an orthomosaic reconstruction software package describing the challenges that the workflow impose for the automatic fault detection algorithms; • Evaluation of the impact of different flight configurations and dataset on the processing time and results; • Application of the proposed framework in real cases, evaluating the challenges imposed by real datasets. As large-scale Photovoltaic (PV) power plants are being expanded in installation number and capacity, aerial infrared thermography (aIRT) has proven to be effective in detecting at different phases of their development, construction and commissioning to operation and maintenance. However, evaluating the aerial imagery over hundreds of hectares fields of PV arrays is very time-consuming and subject to human error. This paper proposes a complete framework for automatically detecting faults in large-scale PV power plants and their physical location inside the plant site. To this end, a Mask-RCNN algorithm is developed and fine-tuned for instance segmentation using a dataset of 93 samples collected in an aIRT flight campaign in Brazil. The results are combined with orthomosaic techniques to create an orthomap of the PV system with the highlighted faults. The proposed method has been tested to automatically detect the faults in two power plants. Several tests were performed to improve the algorithm's accuracy, resulting in high-accuracy results for detecting and localizing hot spots in PV plants and disconnected substrings. The resulting maps could successfully show the location of these faults with high accuracy (10% of false positives). [ABSTRACT FROM AUTHOR]