Anti-pollution flashover of insulator is important for power systems. In recent years, haze-fog weather occurs frequently, which makes discharge occurs easily on the insulator surface and accelerates insulation aging of insulator. In order to study the influence of haze-fog on the surface discharge of room temperature vulcanized silicone rubber, an artificial haze-fog lab was established. Based on four consecutive years of insulator contamination accumulation and atmospheric sampling in haze-fog environment, the contamination configuration appropriate for RTV-coated surface discharge test under simulation environment of haze-fog was put forward. ANSYS Maxwell was used to analyze the influence of room temperature vulcanized silicone rubber surface attachments on electric field distribution. The changes of droplet on the polluted room temperature vulcanized silicone rubber surface and the corresponding surface flashover voltage under alternating current (AC), direct current (DC) positive polar (+), and DC negative polar (−) power source were recorded by a high speed camera. The results are as follows: The main ion components from haze-fog atmospheric particles are NO 3 − , SO 4 2− , NH 4 + , and Ca 2+ . In haze-fog environment, both the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) of insulators are higher than that under general environment. The amount of large particles on the AC transmission line is greater than that of the DC transmission line. The influence of DC polarity power source on the distribution of contamination particle size is not significant. After the deposition of haze-fog, the local conductivity of the room temperature vulcanized silicone rubber surface increased, which caused the flashover voltage reduce. Discharge is liable to occur at the triple junction point of droplet, air, and room temperature vulcanized silicone rubber surface. After the deformation or movement of droplets, a new triple junction point would be formed, which would seriously reduce the dielectric strength of room temperature vulcanized silicone rubber.

Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements are a basic requirement of power systems. In order to predict the site pollution severity (SPS) of insulators, a new method based on random forests (RFs) is proposed. Using mutual information (MI) theory and RFs, the weights of factors related to the SPS of insulators are analyzed. The samples of contaminated insulators are extracted from the transmission lines of high voltage alternating current (HVAC) and high voltage direct current transmission (HVDC). The regression models of RFs and support vector machines (SVM) are constructed and compared, which helps to support the lack of information in predicting NSDD in previous works. The results are as follows: according to the mean decrease accuracy (MDA), mean decrease Gini, (MDG), and MI, the types of the insulators (including surface area, surface orientation, and total length) as well as the hydrophobicity are the main factors affecting both ESDD and NSDD. Compared with NSDD, the electrical parameters have a significant effect on ESDD. For the influence factors of ESDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 52.94%, 6.35%, and 21.88%, respectively. For the influence factors of NSDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 55.37%, 11.04%, and 14.26%, respectively. The influence voltage level (vl), voltage type (vt), polarity/phases (pp) exerted on ESDD are 1.5 times, 3 times, and 4.5 times of NSDD, respectively. The influence that distance from the coastline (d), wind velocity (wv), and rainfall (rf) exert on NSDD are 1.5 times, 2 times, and 2.5 times that of ESDD, respectively. Compared with the natural contamination test and the SVM regression model, the RFs regression model can effectively predict the contamination degree of insulators, and the relative error of the predicted ESDD and NSDD is 8.31% and 9.62%, respectively.

Abstract:With the development of ultra-high voltage (UHV) technology, hybrid reactive power compensation (HRPC) will be widely applied in the future. To study the mechanism by which HRPC influences the characteristics of circuit breakers in UHV transmission lines, this paper establishes an improved electromagnetic coupling transmission line model for out-of-phase and short-line faults. Based on the HRPC equivalent model, a simulation analysis was performed on the characteristics of the circuit breaker when a fault occurs. Using an equivalent lumped parameter circuit, the rate-of-rise of recovery voltage (RRRV) computational formula was deduced and computed. The RRRV variation in the circuit breakers in the system, with and without HRPC, was obtained. Given the circuit breaker interruption characteristics, the research results provide an analysis foundation and a theoretical basis for optimizing the HRPC parameters and selecting the arrangements of circuit breakers in an UHV transmission line.

Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.