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Abstract: Real-time simulation and rapid prototyping of power electronics, critical loads, and control systems have prompted recent interest in accurate electrical terminal models of photovoltaic (PV) panels and array systems. Advancement in computing technologies, such as parallel computing and digital signal processing techniques for real-time simulations have allowed the prototyping of novel apparatus to be investigated in a virtual system under a wide range of realistic conditions repeatedly, safely, and economically. This paper accesses numerical iteration methods, selects appropriate techniques, and combines them with model construction methods well suited for boosting the computational speed of an electro-thermal dynamic model of a PV panel. The target computational engine is a parallel processor based real-time simulator to be used in a power hardware-in-the-loop (PHIL) application. Significant improvements resulting from the proposed modeling approach in computation time and numerical convergence speed are verified using experimental results for the target PV panel using Opal-RT’s RT-Lab Matlab/Simulink based real-time engineering simulator. [Copyright &y& Elsevier]

Identifying Photovoltaic (PV) array faults is crucial for improving the service life and consolidating system performance overall. The strategies based on the supervised Machine Learning (ML) approach represent an attractive solution to identify the PV array faults. However, attainable labeled data to train supervised ML algorithms present challenges in practice. Therefore, this work introduces a novel strategy that employs an ensemble learning concept in conjunction with a semi-supervised learning approach based on a self-training philosophy to realize the faults diagnosis of an arc, line-to-line, power tracker unit, open-circuit, and partial shading, under different of aspects which can directly be impacting faults behavior. The developed ensemble learning paradigm comprises multiple merged ML models, which enhances the overall diagnostics performance. Moreover, it works to alleviate the resource-intensive process, which, in turn, contributes to overcoming standard supervised ML algorithms limitations. To ensure high fault diagnostic capabilities through the proposed fault identification strategy, the principal component analysis is introduced to mitigate the correlation between variables. Moreover, the Bayesian optimization method is adopted to control the behaviors of training ML algorithms, providing models with better characterization results. The merits of the proposed strategy are corroborated through simulation and experimental case studies. [Display omitted] • A novel strategy for effective identification of faults in PV arrays. • The strategy takes into consideration of the minimum number of sensors. • The strategy works to alleviate the resource-intensive process. • The strategy can identify the faults under harsh environmental scenarios. • Strategy effectiveness has been validated by simulation and experimental results. [ABSTRACT FROM AUTHOR]