The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module’s aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module’s performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The aim is to make cogent contributions to the state-of-the-art, identify various critical issues and propose thoughtful ideas for future studies particularly in the area of data-driven analytics. In contrast with statistical and visual inspection approaches that tend to be time consuming and require huge human expertise, data-driven analytic methods including machine learning (ML) and deep learning (DL) models have impressive computational capacities to process voluminous data, with vast features, with reduced computation time. Thus, they can be deployed for assessing module performance in laboratories, manufacturing, and field deployments. With the huge size of PV modules’ installations especially in utility scale systems, coupled with the voluminous datasets generated in terms of EPM and imaging data features, ML and DL can learn irregular patterns and make conclusions in the prediction, diagnosis and classification of PV degradation signatures, with reduced computation time. Analysis and comparison of different models proposed for solar PV degradation are critically reviewed, in terms of the methodologies, characterization techniques, datasets, feature extraction mechanisms, accelerated testing procedures and classification procedures. Finally, we briefly highlight research gaps and summarize some recommendations for the future studies.

The rapid acceptance of solar photovoltaic (PV) energy across various countries has created a pressing need for more coordinated approaches to the sustainable monitoring and maintenance of these widely distributed installations. To address this challenge, several digitization architectures have been proposed, with one of the most recently applied being the digital twin (DT) system architecture. DTs have proven effective in predictive maintenance, rapid prototyping, efficient manufacturing, and reliable system monitoring. However, while the DT concept is well established in fields like wind energy conversion and monitoring, its scope of implementation in PV remains quite limited. Additionally, the recent increased adoption of autonomous platforms, particularly robotics, has expanded the scope of PV management and revealed gaps in real-time monitoring needs. DT platforms can be redesigned to ease such applications and enable integration into the broader energy network. This work provides a system-level overview of current trends, challenges, and future opportunities for DTs within renewable energy systems, focusing on PV systems. It also highlights how advances in artificial intelligence (AI), the internet-of-Things (IoT), and autonomous systems can be leveraged to create a digitally connected energy infrastructure that supports sustainable energy supply and maintenance.

While there is evidence of substantial improvement in efficiency and cost reduction from the integration of Robotics, Artificial Intelligence, and Drones (RAID) in solar installations; it is observed that there is limited oversight by international standards such as the International Electrotechnical Commission (IEC) in terms of the hazards and untapped potentials. This is partly because it is an emerging application and generally burdened with social acceptability issues. Thus, the safety regulations applied are adaptations of device-specific regulations as deemed fit by individual companies. Also, due to the fast-paced technological development of these platforms, there is huge potential for applications that are not currently supported by the device-specific regulations. This creates a multi-faceted demand for the establishment of standardized, industry-wide polices and guidelines on the use of RAID platforms for Solar PV integrations. This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems. Standard safety assurance models and approaches are examined to provide a safe autonomy perspective for Solar PVs. It is considered that, as RAID applications continue to evolve and become more prevalent in the Solar PV industry, standardized protocols or policies would be established to ensure safe and reliable operations.

Electrochemical impedance spectroscopy (EIS) is a non-destructive characterization technique carried out using injected signal analysis. This work investigates the implementation of EIS on photovoltaic (PV) modules using single sinusoid and optimized broadband signals applied via power electronic converter. The procedure applied in this work is carried out to explore rapid online condition monitoring of PV modules using associated power electronic systems. The perturbation signals applied in this work were superimposed using pulse width modulation in a dc-dc boost converter. The injected and output signals were analyzed to determine the impedance of the PV module at various frequencies under normally illuminated and partially shaded conditions. The results obtained were compared to the use of a standard frequency response analyzer (FRA). The results show significant agreement for both the power electronics implementation and FRA tests.

The growth in photovoltaic installations makes characterization and condition monitoring essential. In this paper, broadband impedance spectroscopy is implemented for characterization and performance monitoring of silicon solar cells for near real-time operation. An optimized quasi-logarithmic broadband signal is designed to estimate the impedance response of the cells. Electrical equivalent circuits are then modelled from the obtained Nyquist plots and the cell parameters are extracted using complex nonlinear least squares. This procedure can be applied for direct estimation of the internal parameters of the silicon solar cells/module at different operating points. Results show that the implemented broadband characterization yields good correlation to the conventional electrochemical impedance spectroscopy (EIS) at significantly reduced equipment cost and signal acquisition time (of approximately 1s). Based on the variation of the parameters extracted, new models for n-connected series and parallel cells were proposed towards module or array monitoring. A new condition monitoring parameter was also explored, which is the use of the solar cell critical frequency. It is observed that, the determination of the critical frequencies at various operational conditions can be used to further reduce the frequency scanning range and time for easier online implementation using the photovoltaic system's associated converters.