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In this paper, a fault detection and isolation technique for series arc faults in dc microgrids with multiple power electronics loads is proposed using Unknown Input Observers (UIO). The dc microgrid is first modeled as a linear system using its corresponding graph information, including the oriented incidence matrix. The lines are assumed to be in Pi form (resistive, inductive, and capacitive) defining the dynamic states: line currents and node voltages. Sources and loads are considered inputs to the network and details are presented to include the case where these are not measured. DC series arc faults are then analyzed as disturbances affecting any line. An UIO framework is then presented to detect and identify the faulted line using the network model. Simulation and experimental results are then presented.

Series dc arc fault creates a fire hazard and negative impacts on the distribution bus if not detected and isolated quickly. However, the detection of a series arc fault is challenging due to the low fault current, lack of zero-crossing, and the erratic behavior of arc discharge based on different power electronic loads and controllers in modern power applications. This article presents a practical and versatile series dc arc fault detection method based on ensemble machine learning (EML) algorithms. A buck converter constant power load (CPL) and a boost converter CPL are designed and built to study the different arc fault behaviors and generate training data for the machine learning algorithms. A set of time domain features is extracted from the experimental data and analyzed using the feature importance attribute. An adaptive normalization function then processed the features to mitigate false positive classification caused by load changes. A two-step algorithm is proposed to recognize the arc fault in different load types. Various EML algorithms and the associated hyperparameters are benchmarked to select the most accurate hyperparameters for a detection algorithm for low-cost hardware implementation. Finally, the detection algorithm's effectiveness is verified with CPL testbed.

This paper focuses on analysis and suppression of circulating harmonic currents in a modular multilevel converter (MMC) considering the impact of dead time in medium-voltage applications. A continuous equivalent model of the MMC containing two ideal transformer models is presented. Using this model, the impact of a harmonic voltage upon the dc side is analyzed and the production mechanism of circulating harmonic currents is elucidated. At the same time, the impact of dead time and insulated gate bipolar transistor (IGBT) voltage drop (DTVD) is studied, which indicates that capacitor voltages, output harmonics, and circulating harmonic currents are influenced. Based on this analysis, an open-loop control strategy to suppressing circulating harmonic currents caused by the output current and DTVD is presented. Finally, all these conclusions are verified using a simulation platform with 14 modules per arm fed by a 14-kV dc voltage source and a downscaled experimental platform with four modules per arm fed by a 560-V dc voltage source.

DC arc fault introduces major safety concerns in a wide variety of components in dc networks. However, the randomness and instability of dc arc makes it difficult to be detected. In this paper, an experimental system was designed and tested at different load current, dc source voltage, and gap length to evaluate the impact of each parameter to the dc arc. Based on the experimental results, characteristics in the electrical behavior were studied and fault detection oriented analysis was conducted. A detection algorithm utilizing both time and time-frequency domain characteristics was proposed to differentiate between dc arc fault and normal condition. The detection algorithm was then realized on a digital signal processing board and tested to verify the effectiveness. Experimental results show that the proposed algorithm can detect arc fault in a timely manner and is free of nuisance trip from normal circuit operations such as load change condition.