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.

Pantograph-catenary system is a key component of high-speed railway. Under high speed and strong current operation conditions, the surface roughness of the sliding electrical contact between contact wire and slide changes continuously. It will cause the fluctuation of contact resistance and lower the electrical contact property of the system. Therefore, it is of great significance to study the relationships between the contact resistance and surface roughness characteristics. In this article, lots of current-carrying friction experiments were carried out under different contact pressure, contact current, and sliding speed conditions. A roughness tester was used to measure the roughness of the contact surface after the experiment. Four characteristic parameters were proposed to describe the roughness. They are the root mean square and the maximum peak height of the rough surface, the peak count, and the curvature radius of asperity, respectively. The function relationships between these characteristic parameters and experimental conditions were obtained and discussed. On the basis of Greenwood–Williamson contact model, a new mathematical model of contact resistance in the pantograph-catenary system considering rough surface characteristics was established and verified. It can be used to further study the current conduction mechanism of sliding electrical contact in pantograph-catenary system and evaluate the electrical contact performance.

Timely identification of series arc faults (SAFs) is of vital importance for preventing electrical fires. How to identify SAFs at the output side of a frequency converter (i.e., output-side SAF) is still not clear. A new approach of identifying output-side SAFs by analyzing the output current signals from frequency converters was proposed. First, output-side SAF experiments were performed with harmonic power supplies. Second, the output current signals were decomposed into eight modal components by empirical wavelet transform and an autoregressive model was established. The autoregressive model parameters and the energy ratios of the first three modal components were adopted as the fault features. Finally, an optimized support vector machine was designed and employed to identify SAFs. Comparison tests with similar methods were performed and performance tests under different noise levels and operation conditions were conducted. The test results indicated that the proposed scheme can effectively recognize output-side SAFs. Its runtime is shorter than 1.4 ms. This method provides a reference for the development of industrial three-phase SAF detection devices.