No abstract

Aiming at the difficulty in identifying subtle AC arcs in aviation cables, this paper proposes an arc fault detection method based on the combination of three-dimensional features and convolutional neural network-long short term memory (CNN-LSTM). Firstly, based on the SAE AS5692A standard, the vibration series test, cutting parallel test, and wet arc trajectory parallel test were respectively conducted and the arc current signals under four types of loads were collected to analyze the arc faults under different incentives. Then, the three-dimensional features of arc current including Hurst exponent, inter-harmonic variance, and wavelet energy entropy (H-I-W) were extracted with an improved algorithm so as to enhance the fault identification capability and overcome the limitation of single-dimensional feature detection. Finally, a grid search algorithm was used to find out the optimal parameters, and a three-dimensional reference input CNN-LSTM neural network was designed to detect arc faults. The experimental results showed that the average detection accuracy of the proposed method for the three AC arc faults respectively reached 98.52%, 99.23%, and 98.51%. The real-time performance of the proposed method was better than the comparison methods, proving the feasibility and effectiveness of the proposed method.