Fault Arc Detection in Photovoltaic Systems Based on Autoencoder Model
This paper focuses on the detection of DC series arc faults in photovoltaic systems.A general and relatively low-complexity fault arc detection method is proposed based on the autoencoder deep learning model.Firstly,theoretical simulations are conducted using Matlab/Simulink,and an experimental platform is constructed to simulate real-world scenar-ios of photovoltaic system applications.This experimental platform generates training data for the deep learning algorithm and extracts encoding features from normal operation data.Then,the trained deep learning model is used to analyze the generated fault arc data from the experimental platform.Through the evaluation of various indicators,the performance and effectiveness of the algorithm in detecting DC series arc faults in photovoltaic systems are validated.
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