Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

In order to study the influence of trap parameters on dielectric characteristics of nano-modified pressboards, pressboards were made using the nano doping method with different nanoparticle components. The dielectric characteristics of the modified pressboards were measured, and the trap parameters were investigated using the thermally stimulated current (TSC) method. The test results indicated that the conductivity initially declined and then rose with the increase of nano-Al₂O₃ content, whereas it solely rose with the increase of nano-SiC content. Moreover, the conductivity exhibited nonlinear characteristics with the enhancement of electric field stress at high nanoparticle content. The relative permittivity of modified pressboard declines initially and then rises with the increase of nanoparticle content. In addition, the breakdown strength of modified pressboards exhibited a pattern of incline followed by decline with the increase of nano-Al₂O₃ content, while it always declined with the increase of nano-SiC content. The analysis based on the energy band theory on trap parameters of the constructed multi-core model concludes that the nanoparticle components added in pressboard altered both the depth and density of traps. It is therefore concluded that trap parameters have significant influence on the dielectric characteristics of nano-modified insulation pressboard.

This study investigates the electrical aging characteristics of oil-paper composite insulation. In this paper, a coils model and test system for oil-paper transformer insulation are designed. The electrical aging tests of the model coils at room temperature and operating temperature are performed using the step-stress method. A new method for estimating the unknown parameters in the inverse power exponent model is proposed, and the inverse power model life formula expressed by voltage and electric field strength is obtained. Considering the electrical aging process at the operating temperature, the allowable field strength of the inter-turn insulation of oil-immersed transformers can be improved. The new parameter estimation method proposed in this paper can be used to calculate the unknown parameters of the step-stress solid insulation aging model. This method has a clear physical meaning and can be solved easily. When the stressed voltage is below a certain threshold value, the life of the transformer coil turn-to-turn insulation grease oiled paper model is longer at operating temperatures compared with that at room temperature; when the voltage is higher than the threshold, it presents the opposite tendency.

Traditional thermal sterilization technology is easy to implement and safe, but it will destroy food nutrition and change food taste. Therefore, people began to turn their attention to non-thermal sterilization. High-voltage pulsed electric field sterilization technology is one of them; it has attracted much attention because of its high efficiency and little damage to food. Different types of loads will cause serious trailing of the pulse falling edge. In view of this situation, this paper proposes a new topology circuit that combines a solid-state switch with a half-bridge Marx generator. It can be used for high-voltage pulsed electric field sterilization. By improving the structure of the classical Marx circuit, the high-voltage pulse power supply of the new topology circuit has the characteristics of steep rising edge and short falling edge delay; does not require isolation inductance or isolation resistance, which solves the isolation problem between the DC charging power supply and the high-voltage terminal; and has a good voltage-clamping function and load adaptability. The working process of the topology circuit under resistive, capacitive and inductive loads and the voltage clamping effect when the solid-state switch does not work properly in the discharge process are analyzed in detail. The power supply is composed of an adjustable DC power supply, five-stage half-bridge Marx generator and control protection circuit. A field programmable gate array (FPGA) is used as the controller to generate control signals, and optical fiber isolation is used to provide control signals for the main loop. The power supply can output a high-voltage square wave pulse with a voltage amplitude of 10 kV, maximum pulse number of 1000 per second, maximum pulse width of 20 μs, pulse rise time of smaller than 300 ns and short pulse drop time, and the repeated voltage amplitude, frequency and pulse width are adjustable, which can meet the requirements of a high-voltage pulse sterilization experiment.

In this paper, traveling wave fault location equipment for power cables is designed, and the characteristic waveforms of cable fault point broken down and not broken down are simulated respectively. Then a new traveling wave fault location method based on wavelet transform is presented. Wavelet transform have good performance in denoising and singularity detection, which well solved the difficulty in identifying the initial point of the reflected traveling wave because of the local time-frequency characteristic. The fault distance can be calculated by the round-trip times which the traveling wave spends in the cable. The only required parameter is the length of cable. With the method, the result of fault location is not influenced by the change of propagation velocity of traveling wave. The correctness and effectiveness of this method are analyzed by computer simulation. The obtained results show an acceptable degree of accuracy for fault location.

The prediction of short-circuit current parameters is essential for the adoption of short-circuit fault limiting techniques and the reliable cut-off of circuit breakers. In order to quickly and accurately predict the short-circuit current waveform parameters, a short-circuit fault current prediction method based on ultra-short-time data windows (UDWs) is proposed. First, a mathematical model for describing short-circuit faults is constructed and the characteristics of short-circuit currents are analyzed. Then, the principle of the UDW method for predicting short-circuit current waveform parameters is derived, the correctness of the principle is verified by setting-up an ideal signal through simulation, and the exponential and linear expressions fitted to the curve are analyzed and compared with the improved half-wave Fourier method for predicting current parameters. Finally, trend filtering technology is proposed to eliminate high-frequency interference and white noise interference. The results show that the ultra-short-time data window method can quickly and accurately predict the short-circuit current waveform parameters, where the exponential expression is a better fit to the waveform, and the trend filtering technique enables the elimination of high-frequency and white noise interference in the initial stages of prediction.