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Solar photovoltaics (PV) are susceptible to environmental and operational stresses due to their operation in an open atmosphere. Early detection and treatment of stress prevents hotspots and the total failure of solar panels. In response, the literature has proposed several approaches, each with its own limitations, such as high processing system requirements, large amounts of memory, long execution times, fewer types of faults diagnosed, failure to extract relevant features, and so on. Therefore, this research proposes a fast framework with the least memory and computing system requirements for the six different faults of a solar panel. Infrared thermographs from solar panels are fed into intense and architecturally complex deep convolutional networks capable of differentiating one million images into 1000 classes. Features without backpropagation are calculated to reduce execution time. Afterward, deep features are fed to shallow classifiers due to their fast training time. The proposed approach trains the shallow classifier in approximately 13 s with 95.5% testing accuracy. The approach is validated by manually extracting thermograph features and through the transfer of learned deep neural network approaches in terms of accuracy and speed. The proposed method is also compared with other existing methods., This article belongs to the Special Issue: Advances in Tandem Architectures toward High-Efficiency Solar Cells.

An efficient closed-loop voltage tracking control (VTC) for a grid-forming (GFM)-based converter is required for precise voltage regulation. However, few papers designed a VTC exclusively for MMC with a full understanding of the MMC's internal dynamic coupling and presented a detailed analysis of the controller. In contrast, traditional voltage-current double loop control or its derivatives are employed without considering the impact of the coupled fluctuating capacitor voltage and with insufficient stability analysis under various operating conditions, especially for the wide range of grid short-circuit ratio (SCR). To solve these problems on MMC, this paper systematically analyzes the effect of each component of capacitor voltages on the output voltage. In this paper, an easy-implemental VTC is proposed which takes the dominant factor of capacitor voltage into the feedforward path (CVF). The proposed CVF-VTC with an appropriate feedforward coefficient shows a high damping effect under varying grid strength with the eigenvalue proof of the established small signal model. Moreover, the PI parameters of the VTC show a low effect on system stability when CVF is applied. Experiment results are performed to verify the proposed method.

Power synchronization control (PSC), one of the common grid-forming (GFM) controls, is an emerging grid support technique for converters in the converter-dominated power system. However, its application to modular multilevel converters (MMCs) with dynamic grid conditions and varying grid strength, exposed some instability issues. This paper studies the essence of the MMC internal voltage coupling and the degree of influence on the reference voltage under different grid short-circuit ratios (SCRs). It is found that the key reason for instability is that the sub-module capacitor voltage modifies the reference voltage through the nonlinear switching process and eventually generates the voltage error, up to 61.3% with high SCR = 25. Based on the above findings, an easy-to-implement modulation compensation is proposed to enhance the PSC-MMC stability in changing SCR scenarios. A simplified selection scheme of the compensation coefficient, which is affected by the practical main circuit parameters is presented according to the established small signal model. Both the simulation and the experimental results verify the effectiveness and accuracy of the proposed method.

A novel stochastic strategy for solving the problem of optimal power management of multi-microgrid (MMG) systems is suggested in this paper. The considered objectives are minimizing the total cost and emission of the system. The suggested algorithm is applied on a MMG consisting of four microgrids (MG), each including fossil fuel-based generator units, wind turbine (WT), photovoltaic (PV) panel, battery, and local loads. The unscented transformation (UT) method is applied to deal with the inherent uncertainties of the renewable energy sources (RES) and forecasted values of the load demand and electricity price. The proposed algorithm is applied to solve the power management of a sample MMG system in both deterministic and probabilistic scenarios. It is justified through simulation results that the suggested algorithm is an efficient approach in satisfying the minimization of the cost and the environmental objective functions. When considering uncertainties, it is observed that the maximum achievable profit is about 23% less than that of the deterministic condition, while the minimum emission level increases 22%. It can be concluded that considering uncertainties has a significant effect on the economic index. Therefore, to present more accurate and realistic results it is essential to consider uncertainties in solving the optimal power management of MMG system.

Aiming at the instability problem of traditional power synchronization control (PSC) in a stiff grid, an advanced PSC based on modular multilevel converter (MMC) is proposed in this paper, which can effectively improve converter stability in high short circuit ratio (SCR) scenarios. The key point of the proposed PSC is to eliminate the impact of the arm inductor voltage and capacitor voltage ripple on the converter output voltage, which means a generalized decoupling process in the PSC controller. Based on the decoupling correction, MMC can be treated as a controlled voltage source regardless of the internal impedance circuit and the power oscillation between MMC and the stiff grid can be suppressed effectively. The control strategy is verified and compared with traditional PSC in time simulations.

In order to maximize the use of renewable-based distributed generators (DGs), in addition to dealing with the effects of the inherent power management uncertainties of microgrids (MGs), applying storage devices is essential in the electrical system. The main goal of this paper is to minimize the total operation cost as well as the emissions of MG energy resources, alongside the better utilization of renewable energy sources (RES) and energy storage systems. The uncertainties of wind speed, solar irradiation, market price and electrical load demand are modeled using reduced unscented transformation (RUT) method. Simulation results reveal that, as expected, by increasing the battery efficiency, the achievable minimum daily operational cost of the system is reduced. For example, with 93% battery efficiency, the operational cost equals EUR 9200, while for an efficiency of 97%, the achievable minimum daily operational cost is EUR 8900. Moreover, the proper economic/environmental performance of the suggested approach, which contributes to the possibility of selecting a compromise solution for the MG operator in accordance with technical and economic constraints, is justified.