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Silicone rubber (SR) is high-voltage insulator, but suffers from poor mechanical properties. Nanoparticle addition is a promising way to combat this problem. Herein, SiO2, Al2O3, MgO, SiC and thermally modified SiC nanoparticles were used to improve electrical resistivity, thermal stability and mechanical properties of SR. Surprisingly, electrical resistivity of SR was enhanced by one-order-of-magnitude by incorporation of small amount of nanoparticles, which induced nano-scale traps to capture charge carriers and restrict SR molecules conformation. Tensile strength and modulus of SR increased by SiO2, Al2O3, MgO and SiC incorporation, while decreased elongation at break, as a consequence of agglomeration as detected by microscopic observations. Nano fillers restricted the motion of polymer chains, so storage modules and hardness were increased. Addition of 2 phr SiC resulted in less polarization current comparably. Correspondingly, dielectric breakdown strength of the assigned sample was increased. SiC thermally modified interface is more conductive than the other parts of the matrix reduced charge accumulation and provide more conductive ways for increased charge carriers mobility, dissipate more charges and then increased dielectric breakdown strength. Dynamic-mechanical-thermal analyses demonstrated that storage modulus of SR nanocomposites was comparatively higher than neat SR, owing to rigidity contributed from SiO2 and SiC nanoparticles. Evidently, glass transition temperature shifted to a higher temperature (−109°C) compared to the neat SR (−127°C). Thermogravimetric analysis witnessed superiority of thermal stability of nanocomposites compared to SR, featured by 35°C rise in degradation temperature. The presence of nanoparticles in the polymer matrix acts as a barrier and prevents the release of gaseous products from burning and the entry of oxygen into the system lead to increase thermal stability. [ABSTRACT FROM AUTHOR]

This study presents two different methods under uniform and non‐uniform pollution layer in order to measure and calculate the leakage current (LC) of silicone rubber insulators. Experimental test for evaluating the LC analysis of polluted insulator have been done in a laboratory clean fog chamber. The electric field and potential distributions were obtained from finite element method software for 3D models. The mathematical background and circuit theory are described in details by a section of insulator and using the extended form factor formula. The surface conductivity used in the calculations was extracted from the measured LC after wetting rate. LC characteristics under 1 : 1, 1 : 2, 1 : 5 and 1 : 10 ratios of top to bottom surface salt deposit density on polymeric insulators are studied. To verify the proposed models of this study, the results of experimental data and two other approaches are compared with together before dry‐band formation. Moreover, a dynamic LC model under uniform pollution layer has been introduced and extended in order to calculate the LC when the formation of dry‐bands along the insulator surface occurs. The dynamic model is drawn from experimental data and measured surface conductivity. [ABSTRACT FROM AUTHOR]

In this paper an optimized high frequency lumped model of Induction motor is presented. Model parameters are identified and optimized using Genetic Algorithm (GA). A novel model and approach in an improved high frequency based on GA for parameter identification are used. At first, parameters are limited and then fitted using GA for best fitting. The proposed model considered accurate simulation of both differential and common mode behavior in the EMI-frequency range from 100 Hz to 30MHz. Model parameters which extracted from GA are compared with experimental data in both magnitude and phase at the same time and results show a good accordance between the experimental results and simulation results of the proposed model. A least mean square (LMS) method was used with a GA optimization method to solve the identification problem. The proposed model is suitable to obtain the simulation models to predict high frequency conducted Electromagnetic Interference (EMI), over voltage on terminated motor and common mode current in cable fed induction motor. [ABSTRACT FROM AUTHOR]

• Emphasizing load modeling's critical role in TRV calculation, particularly in substations with short lines. • Discussing load modeling's influence on CB selection, a factor ovelooked in current standards. • Investigating the distinct effects of active and passive loads on TRV calculation in real sub-transmission systems. • Providing a comprehensive system modeling for TRV analysis, incorporating finite element models to compute stray capacitances. In this paper, the selection of circuit breakers for a densely-meshed sub-transmission system with relatively short transmission lines is investigated. The results highlight the significance of modeling the load at the end of lines for accurately calculating the transient recovery voltage (TRV) following the short-circuit current interruption. According to IEEE standards, assuming load modeling at the end of the transmission lines can result in a maximum discrepancy of 20 % in the calculated TRV. Supposing that the load modeling leads to a moderating effect on the calculated TRV, the difference is considered as a safety margin, and load modeling is ignored. Nevertheless, the results presented here illustrate that this assumption strongly depends on characteristics of the studied network and the load type. In a network possessing the characteristics outlined in this paper, even a passive load as small as 15 % of the nominal capacity can significantly moderate the calculated TRV, with impacts reaching up to 38 %. Conversely, modeling an active load may result in a more severe TRV than if it were disregarded. The simulation results indicate that for networks with transmission lines longer than 70 km, neglecting to model passive loads on the transmission lines still yields sufficiently accurate calculations of TRV. [ABSTRACT FROM AUTHOR]