Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.

The respective advantages of AC and DC microgrids lead to the blooming development of the hybrid AC/DC microgrid, which consists of AC and DC microgrid tied by an interlinking converter. A new structure of hybrid coupled interlinking converter (HCIC), which composed of a converter in series with a static VAR compensator (SVC), is proposed for hybrid AC and low voltage DC (LVDC) microgrids. Moreover, a new droop control method is correspondingly developed for the proposed HCIC. Its fundamental active and reactive power flow in the hybrid AC/DC microgrid is controlled by P-δ and Q-P-V-α droop control for converter part of the HCIC, and P-Q-α droop control is proposed for SVC part of the HCIC. Furthermore, a fast harmonic control is given based on a decoupled control strategy to improve the filtering characteristics of SVC part of the HCIC, suppress the harmonic power, and/or inject active power. Finally, simulation and experimental results are provided to verify the proposed HCIC for hybrid AC/LVDC microgrids has the ability of good power flow control and power quality compensation, which not only meets the power flow requirements with low rating system, but also greatly promotes the development of more robust hybrid AC/DC microgrids.

As the photovoltaic (PV) market share continues to increase, accurate PV modeling will have a massive impact on the future energy landscape. Therefore, it is imperative to convert difficult-to-understand PV systems into understandable mathematical models through equivalent PV models. However, the multi-peaked, non-linear, and strongly coupled characteristics of PV models make it challenging to extract accurate parameters of PV models. Metaheuristics can address these challenges effectively regardless of gradients and function forms, and have gained increasing attention in solving this issue. This review surveys different metaheuristics to the PV model parameter extraction and explains multiple algorithms’ behavior. Some frequently used performance indicators to measure the effectiveness, robustness, accuracy, competitiveness, and resources consumed are tabulated and compared, and then the merits and demerits of different algorithms are outlined. The patterns of variation in the results extracted from different external environments were analyzed, and the corresponding literature was summarized. Then, challenges for both metaheuristics and application scenarios are analyzed. Finally, corresponding perspectives on future research are summarized as a valid reference for technological advances in PV model parameter extraction.

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models.

Flux-switching and doubly-salient brushless motors are both novel PM machines having magnets in the stator instead of rotor with outstandingly different static characteristics, especially on the PM flux-linkage waveform, namely, the unipolar and quasi-trapezoidal one for the doubly-salient PM motor, whereas bipolar and sinusoidal one for the flux-switching PM motor, which inevitably results in the distinction in the toque capabilities of both motors. In this paper, based on the theoretical analysis and finite element calculation, a comprehensive comparison on the torque performance of the two motors are investigated under five classical working conditions, in which the flux-switching PM motor is operated in brushless AC mode, while the doubly-salient PM motor is run in brushless DC mode. It is found that the flux-switching PM motor has superior torque capability to that of the doubly-salient PM counterpart, which is verified by both the finite element analysis and experimental results.