In order to study a kind of detection and line selection method of arc fault in actual power supply and distribution lines, arc fault experiments with multi-load loop were carried out. First, five-layer decomposition of main loop current was made by using wavelet packet. The effective coefficients were selected based on the change rate of a wavelet packet energy entropy before and after the arc fault occurs. Then, an effective signal of the arc fault was reconstructed. Second, the effective signal was decomposed into seven independent modes with a variational mode decomposition method. Its time-frequency distribution was obtained by solving the Wigner-Ville distribution and performing a linear summation of each mode. Third, the time-domain and time-frequency features of arc fault were extracted by analyzing the time-domain waveform and time-frequency distribution of the effective signal. An arc fault identification and line-selection model was established by using a support vector machine optimized by particle swarm optimization and grid search. Then, the accuracy of both arc fault detection and line selection were tested. Test results indicated that the proposed method can detect effectively arc fault and select fault line accurately.

Due to the power quality problems and the use of some nonlinear loads such as soft starters, rectifiers, and frequency converters, the circuit current will contain complicated harmonic components, which may affect the identification accuracy of arc fault. Aiming at the interferences of power supply harmonics and nonlinear load noise, a kind of recognition method based on kernel principal component analysis (KPCA) and firefly algorithm optimized support vector machine (FA-SVM) was proposed. KPCA was used to separate the harmonics and load noise interferences in the voltage and current signals. Kurtosis and skewness of the fifth and sixth principal components were used as arc fault features. The FA-SVM was designed to recognize arc fault. The arc fault experiments were carried out under complicated harmonic conditions. The effectiveness of the presented method was verified by experiments.

Series arc fault is one of the important causes of electrical fire in industrial and mining enterprises. It is of great significance to study the series arc fault detection and phase selection feature extraction method to ensure safe and stable operation of electrical equipment and to guide line maintenance. Arc fault experiments under different current and circuit conditions with a three-phase motor and inverter load were carried out. A new arc fault detection and phase selection method based on single-phase current was proposed. First, wavelet threshold noise reduction, piecewise linear fitting, and first-order difference processing were performed on single-phase current signals to filter out noise interference and highlight fault features. Second, fractional Fourier transform (FRFT) was applied to the first-order differential signal to construct the amplitude matrix of the signal from the time domain to the frequency domain. The local features of the amplitude matrix were effectively extracted, and the feature vector of arc fault with lower dimension was established by combining the two-level block singular value decomposition (SVD) method. Finally, an arc fault detection and phase selection model was established using a support vector machine (SVM) optimized by grid search (GS) and particle swarm optimization (PSO) algorithm. The applicability of the model in single-phase multiload was analyzed. The results showed that the proposed method could realize series arc fault detection and phase selection in three-phase motor and inverter circuits, and it can also be used to single-phase multiload circuits.

To quickly and accurately detect the series arc fault (SAF) in three-phase motor with frequency converter load (TMFCL) circuit, a SAF identification model based on convolutional neural network was proposed. The point-by-point isometric mapping was presented to construct input matrix. The lightweight design of the model was realized, respectively, by using bottleneck building block and depthwise separable convolution. A roofline model was used to analyze the complexity and theoretical runtime of the convolution operators. According to the runtime of the operators, the optimal lightweight SAF identification model was determined and labeled as SAFNet. A SAF on-line detection device was designed by deploying SAFNet to an embedded device. And its performance was evaluated by on-line tests. When the sampling frequency is 2.5 kHz, the accuracy is higher than 99.44%, and the runtime is less than 26.48 ms. It can be used to develop arc fault circuit interrupter for the TMFCL circuit.

High-speed train will produce pantograph arc during the driving process, which is harmful to pantograph-catenary system. In order to reduce pantograph-catenary system damage, a method based on Gray Wolf algorithm to optimize the binary Support Vector Machine classifier to identify pantograph arc is proposed. In this paper, 5 groups of pantograph current experiments under different conditions are carried out, and the current data in the pantograph-catenary system under different conditions are measured. The current data state obtained from the pantograph experiments is divided into normal current state and arc current state. Select the mean value, variance, standard deviation, mean value of the first-order difference, and mean value of the second-order difference of the current data as the characteristic value of the pantograph current, and calculate the contribution rate of each characteristic value at the same time, then the current eigenvalue data with a high contribution rate is used as a training sample for learning and recognition through the classifier optimized by Gray Wolf algorithm. The experimental results show that the Gray Wolf optimization algorithm can quickly and accurately identify the pantograph arc, and the classification model obtained is more accurate than the commonly used optimization algorithms such as genetic algorithm and particle swarm. In addition, an engineering implementation of on-line identification of pantograph arc based on industrial computer is proposed.

Sliding friction pair composed of contact wire and pantograph slide is the key component of train current collection system. Contact temperature is one of the important factors affecting the current-carrying and wear performance of the friction pair. In this article, sliding electrical contact experiments under different experimental conditions were conducted. Based on the measured contour curve of the slide surface, two fractal parameters of the slide surface were calculated by using the structure function method. And the effect of contact current, contact pressure, and sliding speed on the fractal parameters was discussed. A temperature field simulation model of sliding electrical contact considering two rough contact surfaces was established with COMSOL Multiphysics software. The effect of surface roughness and fractal parameters on contact temperature was analyzed by simulation. When other simulation parameters keep constant, the contact temperature first drops and then rises as the average roughness height of the contact surface increases. The average roughness slope of the contact surface has little effect on the contact temperature under high speed and strong current conditions. The contact temperature continuously decreases with the increase of fractal dimension or the decrease of fractal roughness. The conclusions can be used as a basis for further research on the contact temperature characteristics of the friction pair to improve its electrical contact performance.