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.