This paper describes a detection algorithm for series dc arc faults to improve safety issues in dc microgrids. Series arc faults typically occur when gaps form in a current-flowing wire or contactor. Arc faults can create serious fire hazards if not detected and removed in a short time. To identify series arc faults accurately and quickly in dc microgrids, a series dc arc fault detection algorithm using relative magnitude comparison is proposed. The proposed algorithm detects the time of occurrence of an arc fault based on the magnitudes of the load current in both the time and frequency domains simultaneously. The algorithm accurately works in a dc microgrid with multiple switching converters, as it relies only on relative current information. The proposed detection algorithm is experimentally verified on a 5-kW 380 V dc microgrid prototype with an arc fault generation unit.

Arc fault detection is important technology to guarantee the safety of power systems and is therefore essential for producing practical power systems for real-world applications. However, fuses and arc fault detection devices (AFDD) struggle to detect series arc faults in DC systems, because the series arc fault induces small current variation between the normal and abnormal conditions. In addition, switching noise from the grid-connected inverter makes detecting arc fault conditions even more difficult. This paper proposes arc fault detection algorithm based on the relative comparison of current variability in terms of frequency spectrum and time series. The operational principle of the proposed algorithm is analyzed to detect the arc fault condition. In addition, the investigation of arc fault impedance using the small-signal modeling can obtain the resonant frequency of arc fault condition at low frequency range. From the impedance model, the frequency analysis range can be designed to avoid the switching noise of inverter. The performance of proposed arc fault detection algorithm is verified with a 3.8 kW grid-connected PV system and arc fault generator.

Arc fault detection is an important process for ensuring the safety of PV and grid-connected inverters and is essential for producing PV systems in real applications. However, it is difficult to detect series dc arc faults using conventional fuses because these faults produce only small current variations. In addition, the arc fault detection devices have limited performance, because they focused on arc fault detection between PV arrays and inverter. Moreover, they require the additional current and voltage sensors to implement the arc fault detection algorithm. This article proposes an arc fault detection algorithm employing differential power processing (DPP) structure. The proposed algorithm only uses intrinsic voltage sensors of DPP and inverter, which can improve the cost-effectiveness of PV systems. In addition, the proposed algorithm can integrate the functionality of maximum power processing for each PV panel and arc fault detection. The voltage relationship between DPP and inverter is analyzed to detect the arc fault condition according to the DPP voltage sensing type. The performance of DPP units and its voltage relationship are verified with the control hardware-in-the-loop system. The arc fault detection performance is verified with the prototype PV system.

The arc-fault phenomenon in photovoltaic (PV) systems has emerged as a major problem in recent years. Existing studies on arc-fault detection in conventional PV systems primarily focus on detecting typical stable arc-faults. Low-energy arc-faults are more challenging to detect than stable arc-faults because of their low current distortions, short durations, and nonlinear properties. These low-energy arc-faults, which are precursors to stable arc-faults, could even inflict serious damage on the system components. Here, a transfer learning-based low-energy arc-fault detection network (TL–LEDarcNet) using a two-stage training method is proposed to proactively detect series DC arc-faults by considering low-energy arc-faults. A one-layer long short-term memory network combined with a lightweight one-dimensional convolutional neural network was developed to detect low-energy arc-faults by only using the sensed current information. The results of offline and online experiments conducted with a commercial grid-connected PV inverter indicate that the proposed method can perform real-time operations on a single-board computer and detect low-energy arc-faults with an accuracy of 95.8%, which is higher than previous methods considered in this study.

This paper proposes a distributed control strategy that considers several source characteristics to achieve reliable and efficient operation of a hybrid ac/dc microgrid. The proposed control strategy has a two-level structure. The primary control layer is based on an adaptive droop method, which allows local controllers to operate autonomously and flexibly during disturbances such as fault, load variation, and environmental changes. For efficient distribution of power, a higher control layer adjusts voltage reference points based on optimized energy scheduling decisions. The proposed hybrid ac/dc microgrid is composed of converters and distributed generation units that include renewable energy sources (RESs) and energy storage systems (ESSs). The proposed control strategy is verified in various scenarios experimentally and by simulation.

A dc arc faultdetection has been studied to improve the reliability of renewable energies and energy storage systems. In this Letter, the control algorithm of dc optimizer is proposed to detect the dc series arc fault condition between the PV panel and dc optimizer. The operational principle of the detection algorithm considers the characteristics of both the PV panel and arc fault, which can detect and extinguish the dc series arc fault condition. The validity of the proposed algorithm is verified through the simulation and experimental results.