DC Series Arc Detection Algorithm Based on Adaptive Moving Average Technique

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.