Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc fault diagnosis algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), and support vector machine (SVM). This method first uses VMD to decompose the current into intrinsic mode functions (IMFs) in the time-frequency domain, then calculates the IMFE according to the IMFs associated with the arc fault. Finally, it uses SVM to detect arc faults according to IMFEs. Arc fault data gathered from a PV arc generation experiment platform are used to validate the proposed method. The results indicated the proposed method can classify arc fault data and normal data effectively.

DC series arc fault detection is essential for improving the productivity of photovoltaic (PV) stations. The DC series arc fault also poses severe fire hazards to the solar equipment and surrounding building. DC series arc faults must be detected early to provide reliable and safe power delivery while preventing fire hazards. However, it is challenging to detect DC series arc faults using conventional overcurrent and current differential methods because these faults produce only minor current variations. Furthermore, it is hard to define their characteristics for detection due to the randomness of DC arc faults and other arc-like transients. This paper focuses on investigating a novel method to extract arc characteristics for reliably detecting DC series arc faults in PV systems. This methodology first uses an adaptive local mean decomposition (ALMD) algorithm to decompose the current samples into production functions (PFs) representing information from different frequency bands, then selects the PFs that best characterize the arc fault, and then calculates its multiscale fuzzy entropies (MFEs). Eventually, MFE values are inputted to the trained SVM algorithm to identify the series arc fault accurately. Furthermore, the proposed technique is compared to the logistic regression algorithm and naive Bayes algorithm in terms of several metrics assessing algorithms’ validity for detecting arc faults in PV systems. Arc fault data acquired from a PV arc-generating experiment platform are utilized to authenticate the effectiveness and feasibility of the proposed method. The experimental results indicated that the proposed technique could efficiently classify the arc fault data and normal data and detect the DC series arc faults in less than 1 ms with an accuracy rate of 98.75%.

Due to the high‐speed switching, the switching process of a silicon carbide (SiC) metal oxide field effect transistor (MOSFET) in a practical circuit is susceptible to parasitics and exhibits significant current and voltage overshoots. This may subsequently lower the efficiency of the device, degrade the reliability of the system, increase the level of electromagnetic interference and prohibit the full utilization of SiC MOSFETs’ potential. Conventional qualitative analysis is difficult to support enough guidance to deal with the switching overshoots in practice. In order to implement quantitative analysis of the switching overshoots of SiC power electronic circuits, an analytical model of SiC MOSFET switching behaviour in the half‐bridge configuration was derived considering all important parasitic parameters, the non‐linear characteristics of the parasitic capacitances and the transconductance coefficient of the device. Based on the derived analytical model, the switching voltage and current overshoots under the specific circuit parameter conditions can be quantitatively evaluated without complex system‐level modelling. Sensitivity analysis for the voltage and current overshoots during the switching transient was quantified. Moreover, the effect rule of the gate resistance on the voltage overshoot was revealed. In the article, simulation and experimental results validate the analytical model and analysis results.