Series arc fault is a common phenomenon in the power system, it will directly affect the working reliability, but there is no mature method to detect it due to its concealment and chaos. Common detection methods that build on the arc fault eigenvectors obtained by manual analysis are subjective and incomprehensive. A series arc fault diagnosis and line selection method based on recurrent neural network (RNN) for a multi-load system was proposed in this paper. Firstly, a series arc fault experiment under a multi-load system was carried out, the training set and test set were built by using the data obtained from the experiment. Then, the RNN model was built, trained, and tested through the training set and test set. Finally, the fast-continuous detection method and the probability-based classification result correction method were proposed, and the detection speed and accuracy were improved much further. The results show that the proposed method is effective for diagnosing series arc fault and line selection under a multi-load system, without analysis of arc fault characteristics.

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.

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.

Series arc fault is one of the main causes of electrical fire. In a three-phase motor with inverter circuit, it is difficult to accurately identify series arc fault occurred in the lines at the back of the inverter, especially when the power supply contains complicated harmonics. To solve this problem, a new feature extraction method of the series arc fault based on the current signal measured at the front of the inverter (CSMFI) was proposed. The series arc fault experiments under different harmonic power supply conditions were carried out in the three-phase motor with inverter circuit. Five-layer decomposition was performed on the CSMFI by using empirical wavelet transform. The attractor track matrix (ATM) of each decomposed signal was established. The fault features of the series arc fault were obtained by calculating and selecting the singular values of the ATM. The series arc fault was identified by using an optimized support vector machine. The effectiveness of the method and its applicability under different experimental conditions were tested. The method can simultaneously identify the series arc fault occurred in the lines at the front or back of the inverter by analyzing the CSMFI. It also has strong anti-interference ability.