An Integrated Multi-Feature Algorithm for Arc Fault Detection

Arc fault is an important cause of electrical fire in distributed power generation and smart grid, which has caused significant property losses and personal injuries. Traditional protective devices such as circuit breakers and fuses cannot effectively detect low-current arc faults, so it is necessary to develop fast and accurate detection methods. Therefore, this paper proposes an integrated multi-feature arc fault detection method, which combines time-domain and frequency-domain features and uses a multi-level discrimination method to improve detection accuracy. The proposed method includes using numerical calculations and fast Fourier transform algorithms to extract key features from the current waveform, such as waveform factor, kurtosis, fundamental frequency harmonic ratio, and spectral centroid, for comprehensive analysis to improve the accuracy of fault detection. Its uniqueness lies in the use of feature complementarity and volatility to enhance fault recognition under complex load conditions. The integrated multifeature detection device implemented using the STM32 microcontroller was tested on an arc fault testing system. The results show that the proposed method has good detection effects under various load scenarios, and its response time in all tests included in the standard is far below the specified threshold. The proposed algorithm significantly improves the accuracy and response speed of arc fault detection and further reduces hardware complexity for practical application.