UL 1699B 기반 태양광 DC 직렬 아크 검출에 필요한 FFT와 DWT 기법 비교 분석 연구
Comparative Analysis of FFT and DWT Techniques Required for UL 1699B-based Photovoltaic DC Series Arc Detection
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초록
Considering the dangers of DC serial arcs in PV systems and the time when arcs are caused by electrical fire, we aimed to quickly detect DC serial arcs within 0.5 s. To quickly analyze and identify the frequency characteristics of arcs, fast Fourier transform (FFT) and discrete wavelet transform (DWT), which have been the most widely used frequency analysis methods in recent years, were compared and analyzed to implement an algorithm for detecting the most accurate and efficient DC arc. In this study, an experiment was conducted based on the UL 1699B arc test conditions. At this time, the arc energy does not exceed 750 J, and the arc must be detected within 2.5 s. In the overall process, components other than the arc were removed by applying a band pass filter to detect a specific frequency component of the arc. ADC was performed to convert the current value into a digital signal, and signal processing was performed using FFT and DWT using the filtered signal. Finally, after calculating the energy, which is a component of the frequency for each specific period, the threshold of each energy value was set to determine whether the arc exceeds a specific threshold.
Abstract
Considering the dangers of DC serial arcs in PV systems and the time when arcs are caused by electrical fire, we aimed to quickly detect DC serial arcs within 0.5 s. To quickly analyze and identify the frequency characteristics of arcs, fast Fourier transform (FFT) and discrete wavelet transform (DWT), which have been the most widely used frequency analysis methods in recent years, were compared and analyzed to implement an algorithm for detecting the most accurate and efficient DC arc. In this study, an experiment was conducted based on the UL 1699B arc test conditions. At this time, the arc energy does not exceed 750 J, and the arc must be detected within 2.5 s. In the overall process, components other than the arc were removed by applying a band pass filter to detect a specific frequency component of the arc. ADC was performed to convert the current value into a digital signal, and signal processing was performed using FFT and DWT using the filtered signal. Finally, after calculating the energy, which is a component of the frequency for each specific period, the threshold of each energy value was set to determine whether the arc exceeds a specific threshold.
참고문헌 (5)
- Kil, G. -S., "Detection Method of Series Arc Signal", The Korean Society For Railway, 11(5), pp.477~481 (2008)
- Hatton, P. C., "Arc Generator for Photovoltaic Arc Fault Detector Testing", IEEE PVSC 43th, pp.1702~1707 (2016)
- Johnson, J., "Photovoltaic DC Arc Fault Detector Testing at Sandia National Laboratories", IEEE Photovoltaic Special Conf, pp.3614~3619 (2011)
- Kim, S. -K., "Development of Arc Detection Algorithm for 50kW Photovoltaic System", The Transactions of the Korean Institute of Electrical Engineers, 67(1), pp.27~32 (2018)
- Ahn, J. -B., "Seperation Inverter Noise and Detection of DC Series Arc in PV System Based on Discrete Wavelet Transform and High Frequency Noise Component Analysis", The Transactions of the Korean Institute of Electrical Engineers, 26(4), pp.337~345 (2021)