DC fault arc occurring in low voltage DC systems such as photovoltaic (PV) system and battery energy storage systems is difficult to be extinguished. The traditional arc fault identification approaches have low recognition accuracy for the series arc. Therefore, a reliable approach is needed to detect DC fault arc timely and accurately. In this paper, the time and frequency domain characteristics of the current signal of a low-voltage DC system circuit are analyzed. A comprehensive algorithm based on the K-line and spectrum integration difference of the current is proposed, which can detect the low voltage DC fault arc, and effectively distinguish four states of the circuit: normal current, DC arc, switch action and load mutation. The real time arc detection test is performed using a microcontroller STM32F407. In the experiment, the fault arc can be detected and isolated within 37 ms, which is in line with the UL1699B standard, and the identification accuracy can reach 99.17%.

Photovoltaic systems provide electrical power with reduced emissions at competitive costs compared to legacy systems. A low or medium voltage dc distribution system is usually used for solar integration. In dc systems, parallel and series arc faults are a safety concern. Thus, reliable and timely detection and mitigation of arc faults are critical. DC arc detection methods typically use time or frequency spectrum variations of the circuit current or voltage to differentiate the arcing event from other system events. Since practical systems include power electronics and maximum-power-point tracking, any detection scheme must perform robustly in the electrical environment that these components establish in the dc power system. A capacitor placed in parallel with the main system is an effective sensor for series arc fault detection and localization applicable in this complex electrical environment. This article shows that the analysis of the amplitude, polarity, and spectrum characteristics of the capacitor current and voltage resulting from perturbations caused by the arc provides an effective method to identify and localize faults. The detection accuracy of the proposed approach is 98.3% and the localization accuracy rate is 100% for the correctly detected faults.

DC arc fault occurring in a photovoltaic (PV) system may cause fire accidents, which should be detected and isolated timely. A novel DC arc fault detection method using Pseudo Wigner-Ville distributed (PWVD) algorithm is proposed. Arc fault experiment platform in a 5 kW grid connected PV system is built, and the circuit current of the PV strings under different test conditions are measured. The PWVD algorithm is used to analyze the currents. The time frequency graph of the current can be obtained, and the characteristic parameters for arc fault detection are extracted. The PWVD results can intuitively reflect the time, frequency and amplitude information of the current. The proposed method can detect DC arc fault in PV system accurately.

DC arc fault occurs in the DC system due to the loose connection or contact, broken wire insulation, and other reasons. DC arc is difficuit to be extinguished and detected. The high temperature arc is easy to produce the fire, which may cause severe damage to the DC system. In this paper, an experimental platform for DC arc is established to investigate the effects of loop current and electrode spacing on the arc characteristics. The volt-ampere characteristic curve of the DC arc is obtained by fitting. The frequency spectrum of arc current is analyzed, and the high frequency components of arc current are extracted. The equivalent model of arc is established, including the steady state impedance and high frequency characteristic models. Then the equivalent model of arc is established in the MATLAB/Simulink. The simulation results are consistent with the experimental data, which verified the correctness of the arc model.

As the DC power system is more and more widely used in electric vehicles, aerospace, electric ships and energy storage systems. DC arc faults occur frequently in these systems. Therefore, it is necessary to analyze the characteristics of the dc arc and detect the arc fault timely. Firstly, a low voltage dc arc fault experiment platform is built, and the voltage and current of the DC arc under different test conditions including load types, circuit current amplitude, electrode material and electrode spacing are measured. The amplitude and frequency spectrum of the arc current of the DC arc are analyzed. Additionally, the influence of parameters on the high frequency characteristics of the arc current are investigated. Finally, based on the characteristics of DC arc current, the characteristic parameters of arc fault detection are extracted, and a DC arc fault detection method is proposed.

To find out a more effective way to detect the overcharge of Lithium-ion battery (LIB), using the LIB module, heat transfer module and mechanics module, the electrochemical-thermal-mechanical coupling model of LIB is constructed in the COMSOL Multiphysics. The temperature and deformation of LIB under different state of charge (SOC) including overcharge are studied using the coupling model. The simulation results show that when the LIB is overcharged, the deformation of the battery shell will increase accompanied with a large temperature increase. In addition, the temperature and deformation of the overcharged LIB under different charge currents and ambient temperature are studied using the coupling model, and the results show that both the charge current and the ambient temperature have some effect on the overcharged battery. With the increase of the charge current and ambient temperature, the deformation of the battery shell will increase and the rate of battery temperature rise will be larger.