Real-Time DC Series Arc Fault Detection Based on Noise Pattern Analysis in Photovoltaic System

This article presents a method for detecting series arc faults based on noise pattern analysis in photovoltaic systems. The arc detection circuit is required to detect the series arc quickly and not falsely detect the system noise as arc noise. This article proposes noise pattern analysis to distinguish between system noise and arc noise and prevent false detection. First, periodic feature analysis prevents false detection by periodic noise, such as inverter noise, using the difference in periodic characteristics. Second, zero-range density (ZRD) analysis detects series arc noise using the difference in fluctuation characteristics. An integrated arc fault detection algorithm comprising discrete wavelet transform decomposition, periodic feature analysis, ZRD, and an iterative loop algorithm is developed. The proposed methods are verified using noise distinction experiments, wherein the injected signal and arc noise are distinguished with high classification precision using the proposed noise pattern analysis method. The developed arc detection algorithm is applied to a real-time series arc detection board based on the TMS320F28335 DSP, and the performance of the series arc detection and false detection prevention is verified using the UL1699B arc detection test circuit and a real photovoltaic system.