This paper proposes a method based on chaotic spread spectrum sequence for synchronous online diagnosis of intermittent faults for complex and cable networks, such as the aircraft power system. The novelties of the proposed method include: 1) Since chaotic spread spectrum signals are sensitive to initial values, the number of chaotic sequences are not limited and the proposed method is not limited by the number of cables under monitoring; 2) With the self-correlation characteristic and cross-correlation characteristic of chaotic sequence, the proposed method can be extended to online diagnosis of multiple cable intermittent faults; and 3) The signal source impedance amplification method is proposed for intermittent arc faults of multiple cables, which improves the reflection coefficient and increases the accuracy of on-line detection and location of intermittent arc faults. A series of simulations and experiments on multiple cables with different intermittent fault modes are conducted to verify the effectiveness of the proposed approaches.

Due to the electrical, thermal, and mechanical stress, the insulating materials of cable undergo a slow aging process in service, which leads to the decline of insulation performance. Besides, friction frequently occurs when the cable is moving or vibrating, which will easily cause mechanical damage including frays and chafes. Both aging and damage will cause the characteristic impedance to change. Therefore, it is of great significance for the safe operation of the cable to analyze the variations of characteristic impedance and to make an effective diagnosis. In this article, a two-dimensional model of the cable containing defects is first built, and the characteristic impedance of the model is calculated by the finite-element method (FEM). Meanwhile, a quantitative analysis of the characteristic impedance under two defect types is carried out. Second, combining the space-time focusing of time-reversal and the time-frequency characteristics of reflectometry, the time-reversal time-frequency domain reflectometry (TRTFDR) is proposed to diagnose the cable faults. Based on the distributed parameters calculated by the FEM, the TRTFDR simulation model is established to study the judgment of different faults. Finally, the validity of this method is verified by experiments.

Series arc fault (SAF) has severe impacts on the safety of dc power supply systems. Timely and accurate SAF detection under different operating conditions is an open and challenging problem. To address this problem, this article proposes an integrated SAF detection method for different operating conditions. In the proposed method, dual-tree complex wavelet transform (DT-CWT) is employed to obtain an accurate current signal decomposition. The singular values of each wavelet component are then extracted by using an improved matrix construction method, which can effectively reduce the computational cost of constructing the high-dimension features. Finally, the kernel extreme learning machine (KELM) is applied to fuse the feature information for SAF detection. A series of experiments are presented to demonstrate the effectiveness of the proposed method. The results of offline experiment show that the accuracy of the proposed method has higher detection accuracy than that of the six state-of-the-art methods under different operating conditions. In this article, the proposed method is then embedded into the hardware of the experimental platform for online in-service implementation. The online experimental results show that the proposed method achieves fast and accurate SAF detection and, at the same time, offers outstanding reliability and stability in system dynamic transients.

The physical properties in amorphous regions are important for the insulation aging assessment of polytetrafluoroethylene (PTFE) cable insulation materials. In order to study the effect of boron nitride (BN) nanoparticles on the physical properties of PTFE materials under moisture, temperature, and electric fields conditions at the molecular level, the amorphous region models of PTFE, BN/PTFE, water/PTFE, and water/BN/PTFE were respectively constructed by molecular dynamics (MD) simulation. The mechanical properties including Young’s modulus, Poisson’s ratio, bulk modulus, and shear modulus, along with glass transition temperature, thermal conductivity, relative dielectric constant, and breakdown strength of the four models have been simulated and calculated. The results show that the mechanical properties and the glass transition temperature of PTFE are reduced by the injection of water molecules, whereas the same, along with the thermal conductivity, are improved by incorporating BN nanoparticles. Moreover, thermal conductivity is further improved by the surface grafting of BN nanoparticles. With the increase of temperature, the mechanical properties and the breakdown strength of PTFE decrease gradually, whereas the thermal conductivity increases linearly. The injection of water molecules increases the water content in the PTFE materials, which causes a gradual increase in its relative dielectric constant. This work has shown that this effect is significantly reduced by incorporation of BN nanoparticles. The variation of physical properties for PTFE and its composites under the action of moisture, temperature, and electric fields is of great significance to the study of wet, thermal, and electrical aging tests as well as the life prediction of PTFE cable insulation materials.

This paper develops and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry (SSTDR) for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to restrict the detection signal from transmitting to power source and to eliminate the effect of the power source impedance mismatch. Moreover, a novel structure with two parallel couplers is developed to enhance the effect of capacitive trapper with small capacitance. The development, design, analysis, and implementation of the proposed approach are discussed in details. A series of simulations and experiments on cables with different fault modes are conducted, along with comparison with existing capacitive coupling, to verify and demonstrate the effectiveness of the proposed method.

Arc faults are one of the important causes of electric fires. In order to solve the problem of randomness, diversity, the concealment of series arc faults and to improve the detection accuracy, a novel arc fault detection method integrated random forest (RF), improved multi-scale permutation entropy (IMPE) and wavelet packet transform (WPT) are designed. Firstly, singular value decomposition (SVD) was applied to filter the current signal and then the high-dimensional fault features were constructed by extracting IMPE, the wavelet packet energy and the wavelet packet energy-entropy. Afterward, the high-dimensional fault features were employed to train the RF to realize the arc fault detection of different load types and the experimental results verify the effectiveness of the arc fault detection method designed in this paper. Finally, the comparative experiments demonstrates that the RF shows better performance in arc fault detection compared to the back-propagation neural network (BPNN) and least squares support vector machines (LSSVM), and that the experiments of transient events indicate that RF is able to effectively avoid incorrectly detecting different load types during the start operations and stop operations.