SunSpark: Fusion of time-domain and frequency-domain transformer for accurate identification of DC arc faults

Abstract

Photovoltaic (PV) power generation is pivotal to the energy strategies of various nations, yet it is plagued by significant security challenges. This paper proposes a large-scale neural network model that integrates time-domain and frequency-domain techniques for the detection of arc faults in PV systems. The algorithm leverages sequence decomposition to extract trend information from current signals, and then applies the Fourier transform to convert various encoded data into the frequency domain. Due to the sparsity of frequency-domain information, the computational cost of extracting and processing information in the frequency domain is minimal, resulting in high efficiency. The selectively extracted information is then input into a separate lightweight classifier for classification and recognition. The proposed intelligent framework not only effectively filters out high-frequency noise signals, but also demonstrates strong robustness against various disturbances, yielding exceptional recognition performance with an accuracy rate consistently surpassing 97$ \% $. Code and data are available at this repository: https://github.com/yixizhuimeng?tab = projects.

Keywords

• signal processing
• arc fault detection
• artificial intelligence
• time-domain and frequency-domain fusion