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In two-stage grid-integrated photovoltaic (PV) system, usually a DC-DC converter is employed between the PV modules and the inverter. The dynamic interactions between the DC-DC converter, inverter, and the maximum power point tracking (MPPT) controller may affect the system performances. This study gives an integral procedure to design a stable sliding-mode controller (SMC) based on fixed frequency equivalent control approach to improve the transient response of PV system and to track the reference voltage supplied by the voltage-oriented MPPT controller in the presence of environmental and load perturbations and converter output sinusoidal perturbations imposed by the second harmonic of the grid frequency. The controller consists of fast current tracking inner current loop based on SMC law whose sliding surface is defined by the input capacitor and inductor current and outer PI controller maintains required PV voltage regulation. The superiority of the controller is validated at different operating conditions through PSIM software and its performance is compared with variable frequency hysteresis-based SMC. To check the static and transient performances of the system, various experiments are conducted. The results obtained show very fast transient response in settling time and alleviation of chattering magnitude at various operating conditions.

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Every year, each solar panel suffers an efficiency loss of 0.5% to 1%. This degradation of solar panels arises due to environmental and electrical faults. A timely and accurate diagnosis of environmental faults reduces the damage caused by faults on the panel. In recent years, deep learning precisely convolutional neural networks have achieved wonderful results in many applications. This work is focused on finely tuning pretrained models of convolutional neural networks, especially AlexNet, GoogleNet, and SqueezeNet. Based on the performance metrics, SqueezeNet is used for training thermal images of solar panels and for the classification of environmental faults. The results obtained show that SqueezeNet has a significant testing accuracy of 99.74% and F1 score of 0.9818, which make the model successful in identifying environmental faults in solar panels and help users to protect the panels.

This article presents a reliable and efficient photovoltaic sliding mode voltage-controlled maximum power point tracking DC-DC converter-active power filter integration system to supply real power to grid. This integrated active power filter system performs power quality enhancement features to compensate current harmonics to make distortion-free grid supply current and reactive power employing nonlinear loads. The proposed proportional-integral-derivative-based sliding mode controller is designed with fixed-frequency pulse-width modulation based on equivalent control approach. The main objective of this paper is to design a photovoltaic system with a new sliding surface to force the photovoltaic voltage to follow the reference maximum power point voltage with the alleviation of slow transient response and disadvantages of chattering effects of variable-frequency hysteresis modulation sliding mode controller-maximum power point tracking. The perturbations caused by the uncertainties in climatic conditions and converter output bulk oscillations during grid integration are also mitigated. The features of the proposed photovoltaic-active power filter integration system are confirmed at different operating conditions through PSIM simulation software, and its performance is also compared with a conventional variable-frequency sliding mode-controlled maximum power point tracking. The obtained simulation and experimental results give good dynamic response under various operating conditions of environmental and local load conditions.