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Abstract

The single-phase voltage loss is a common fault. Once the voltage-loss failure occurs, the amount of electrical energy will not be measured, but it is to be calculated so as to protect the interest of the power supplier. Two automatic calculation methods, the power substitution and the voltage substitution, are introduced in this paper. Considering the lack of quantitative analysis of the calculation error of the voltage substitution method, the grid traversal method and MATLAB tool are applied to solve the problem. The theoretical analysis indicates that the calculation error is closely related to the voltage unbalance factor and the power factor, and the maximum calculation error is about 6% when the power system operates normally. To verify the theoretical analysis, two three-phase electrical energy metering devices have been developed, and verification tests have been carried out in both the lab and field conditions. The lab testing results are consistent with the theoretical ones, and the field testing results show that the calculation errors are generally below 0.2%, that is correct in most cases.
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Abstract

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological filtering method are used to process the images. The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the 3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance. The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance. In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.
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