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An effective device to reduce the earth resistance of grounding systems for power transmission towers on rocky mountain tops with high soil resistivity and constrained area is proposed in this paper. In these scenarios, conventional methods such as using horizontal electrodes to reduce ground resistance require more space and fine‐grained soil for backfill. These methods are not applicable to scenarios where the ground consists of rock or crushed stone. To tackle such limitations, we propose an effective approach with less space and soil requirements. Based on the steady electric current field theory and the electrostatic field, the proposed approach designs a central grounding system by a special steel reinforcement cage. The layout of the cage is strictly based on theoretical calculations. The resistance of the central grounding system can be obtained by calculation and measurement. Results show that this scheme can reduce ground resistance effectively in comparison with the traditional method of imbedding horizontal radial electrodes in high‐resistivity soil on mountain tops where space is limited and excavation conditions are poor. The proposed method has also been validated in practical applications. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

All the DS18B20 sensors, used for the multipoint test temperature, are connected with MCU on one of IO bus, and temperature data are collected by turns. If the system has a large amount of sensors, the time of MCU used in processing the temperature data is obviously prolonged, so the cycle of alternate test gets longer. In this paper, a new method that DS18B20 are rationally grouped is presented, and some measures are taken in software; as a result, the speed of alternate test advances distinctly.Key words-DS18B20 Group ,temperature test, time spent on the alternate test.I.

A new method for the diagnosis of incipient transformer faults based on a combined decision model using IEC 60599 three-ratio method, grey system theory (GS) algorithm, Dempster-Shafer evidence (DS) theory, and backpropagation (BP) neural network diagnosis method is proposed in this paper. The new method will eliminate the influence of any one individual diagnosis method due to insufficient information or diagnostic prejudices. In order to minimize the sum-of-squared errors of the proposed method, the optimal weights of every individual method which comprises the combined decision model are calculated based on their own diagnosis error ratios. Thus a combined decision model based on optimal weights (CDMOW) is formed. Then the optimal diagnosis result is obtained based on CDMOW. Results of diagnosis instances show that the use of CDMOW not only gives a much higher diagnostic accuracy rate but also more stability in fault diagnosis than any single method composing it.