The wide variety of arc faults induced by different load types renders residential series arc fault detection complicated and challenging. Series dc arc faults could cause fire accidents and adversely affect power systems if not promptly detected. However, in practical power systems, they are difficult to detect because of a low arc current, absence of a zero-crossing period, and various abnormal behavior based on different types of power loads and controllers. In particular, conventional protection fuses may not be activated when they occur. Undetected arc faults could cause false operation of power systems and potentially lead to damage to property and human casualties. Therefore, it is imperative to develop a detection system for series arc faults in DC systems for the reliable and efficient operation of such systems. In this study, several typical loads, especially nonlinear and complex loads such as power electronic loads, were chosen and analyzed, and five time-domain parameters of the current—average value, median value, variance value, RMS value, and distance of the maximum and minimum values—were chosen for arc fault detection. Various machine learning algorithms were used for arc fault detection and their detection accuracies were compared.

This paper proposes a DC series arc detection algorithm in a photovoltaic (PV) system using an adaptive moving average (AMA). The proposed algorithm uses two moving averages of F _av which is the average of 5 kHz to 40 kHz frequency band. One is MA _small which is the moving average highly affected by recent F _av . The other is MA _large which is the moving average heavily affected by past F _av . There is a little difference between MA _small and MA _large before arcing because F _av is approximately constant. However, this difference increases when the arc occurs because MA _large slowly follows MA _small . This difference is used as an arc detection indicator (ADI) in this study. Additionally, AMA is proposed to avoid nuisance tripping in the normal transient state. The proposed method determines the arc occurrence using the relative magnitudes of the two moving averages. Therefore, it is less affected by the shape of the frequency fluctuations caused by the load inverter. Hence, the proposed algorithm is effective in the centralized and spread-type of frequency fluctuations. These results were verified through an arc detection test and nuisance tripping test using arc experimental data and MATLAB.

In this study, the frequency characteristics of series DC arcs are analyzed according to the types of frequency fluctuations caused by inverters in photovoltaic (PV) systems. These frequency fluctuation types used in analysis include centralized frequency fluctuations by three-phase inverter, spread frequency fluctuations by three-phase inverter, and centralized frequency fluctuations by single-phase inverter. To collect arc current data, the frequency fluctuations are generated by inverters in the arc-generating circuit, designed by referring to UL1699B, and the arcs are generated by separating the arc rods of the arc generator. The frequency analysis of the arc current data, collected using an oscilloscope, is conducted using MATLAB. From the results of the frequency characteristics analysis, it is confirmed that the frequencies in the range from 5 to 40 kHz increase after arc generation regardless of the type of frequency fluctuation. In addition, the smaller the current, the greater the increase in frequencies between 5 and 40 kHz after arc generation. Further, in case of arc currents with centralized frequency fluctuations, for larger switching frequencies, the 5 to 40 kHz components increase to a greater extent after arcing.

This paper proposes a series and parallel DC arc detecting and identifying (SPADI) technique using the frequency features of the load current ( $I_{L}$ ) and the load voltage ( $V_{L}$ ). The frequency changes in $V_{L}$ and $I_{L}$ under the series and parallel arc are different. The parallel arc can only raise the high-frequency components of $V_{L}$ . In contrast, the high-frequency components of $I_{L}$ can be increased in both series and parallel arcs. However, the increasing frequency range is different. The high-frequency components of $I_{L}$ generated by the series arc are concentrated in the 5 kHz to 40 kHz band. Meanwhile, the high-frequency components of $I_{L}$ caused by the parallel arc are observed evenly in all frequency ranges. Inspired by these features, $V_{L}$ ’s 5 kHz to 100 kHz components are used to sense the parallel arc in the proposed technique. In addition, the series arc is detected using the 5 kHz to 40 kHz and 50 kHz to 100 kHz bands of $I_{L}$ . Experimental tests verified the proposed algorithm implemented at the digital signal processor (DSP). As a result of 160 repeated tests, the probabilities of detecting the series and parallel arc were 100 % and 96.25 %, respectively. Moreover, the probability of correctly identifying the arc type when detecting the arc was 100 %. Also, the average detection times of the series and parallel arc were 0.11 s and 0.16 s, respectively.

This paper proposes a lifespan extension technique for three-phase voltage inverters using hybrid offset voltage. The proposed method lengthens the inverter lifetime by independently adjusting the switching frequency of the three phases in accordance with the aging degree. To reduce the switching operation of the phase with the shortest lifetime, the proposed technique injects the offset voltage for generalized discontinuous pulse-width modulation PWM (GDPWM) into the reference voltage in the region where the switching operation of the shortest lifespan phase can be stopped. When the switching operation does not need to be stopped, the offset voltage for space vector PWM (SVPWM) is injected into the reference voltage for high inverter load current quality. An offset voltage that varies according to the need to stop the switching operation is the proposed hybrid offset voltage. Using the proposed hybrid offset voltage, the switching frequencies of the three phases are independently controllable. In addition, since only the switching operation of the phase having the shortest lifespan is reduced, the load current quality in accordance with the switching operation reduction is good compared to the conventional method to simultaneously diminish all phase switching frequencies. The proposed method significantly increases the reliability of the three-phase voltage inverter, where the thermal stress of the phase having the shortest lifespan is decreased up to 55%, whereas the inverter lifetime can be increased by 10 times. The proposed technique was verified by simulations and experiments.