Abstract-The electrical contact resistance greatly influences the thermal behavior of substation connectors and other electrical equipment. During the design stage of such electrical devices it is essential to accurately predict the contact resistance to achieve an optimal thermal behavior, thus ensuring contact stability and extended service life. This paper develops a genetic algorithm (GA) approach to determine the optimal values of the parameters of a fractal model of rough surfaces to accurately predict the measured value of the surface roughness. This GA-optimized fractal model provides an accurate prediction of the contact resistance when the electrical and mechanical properties of the contacting materials, surface roughness, contact pressure and apparent area of contact are known. Experimental results corroborate the usefulness and accuracy of the proposed approach. Although the proposed model has been validated for substation connectors, it can also be applied in the design stage of many other electrical equipment.

In the last years there has been a considerable increase in electricity consumption and generation from renewable sources, especially wind and solar photovoltaic. This phenomenon has increased the risk of line saturation with the consequent need of increasing the capacity of some power lines. Considering the high cost and the time involved in installing new power lines, the difficulty in acquiring tower sites and the related environmental impacts, some countries are considering to replace conventional conductors with HTLS (High-Temperature Low-Sag) conductors. This is a feasible and economical solution. In this paper a numerical-FEM (Finite Element Method) approach to simulate the temperature rise test in both conventional and high-capacity substation connectors compatible with HTLS technology is presented. The proposed coupled electric-thermal 3D-FEM transient analysis allows calculating the temperature distribution in both the connector and the conductors for a given current profile. The temperature distribution in conductors and connectors for both transient and steady state conditions provided by the proposed simulation method shows good agreement with experimental data.

Dynamic thermal line rating (DTLR) allows us to take advantage of the maximum transmission capacity of power lines, which is an imperious need for future smart grids. This paper proposes a real-time method to determine the DTLR rating of aluminum conductor steel-reinforced (ACSR) conductors. The proposed approach requires a thermal model of the line to determine the real-time values of the solar radiation and the ambient temperature, which can be obtained from weather stations placed near the analyzed conductors as well as the temperature and the current of the conductor, which can be measured directly with a Smartconductor and can be transmitted wirelessly to a nearby gateway. Real-time weather and overhead line data monitoring and the calculation of DTLR ratings based on models of the power line is a practical smart grid application. Since it is known that the wind speed exhibits important fluctuations, even in nearby areas, and since it plays a key role in determining the DTLR, it is essential to accurately estimate this parameter at the conductor’s location. This paper presents a method to estimate the wind speed and the DTLR rating of the analyzed conductor. Experimental tests have been conducted to validate the accuracy of the proposed approach using ACSR conductors.

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