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Zeng X.,Changsha University | Zhang W.,Changsha University | Xu S.,Hunan Hydro and Power Design Institute HHPDI | Gu Y.,Changsha University | Zhang B.,Hunan Province Planet Intelligent Electricity Development Company
Proceedings of the 5th IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, DRPT 2015 | Year: 2015

This The development of human's living condition and the corresponding technology application in low-voltage distribution system have currently caused lots of power consumption. Thus, it is important to find out the fault location and switch off the device before fault occurring. This paper analyzes the principle of leakage current & short-circuit current fault detection, applies fault pre-detecting theory to preclude the hidden fault, provides the measure of isolating force electricity from weak electricity, designs a novel household distribution protection device with fault pre-detecting. Its performance meets relevant low-voltage distribution system standards. Simulation and practical application show that the device has a bigger advantage of troubleshooting, correct malfunction for relays. Therefore the device is more smart and practical than traditional protection device and has more value for popularization. © 2015 IEEE.


Gu Y.,Changsha University | Zeng X.,Changsha University | Xu S.,Hunan Hydro and Power Design Institute HHPDI | Deng S.,Hunan City University
Proceedings of the 5th IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, DRPT 2015 | Year: 2015

In China, the method of neutral resonant grounding and neutral ungrounded are widely used in distribution networks. When grounding fault occurred in distribution network, the fault current is small, and vulnerable to the outside interferences, so the fault features are difficult to be detected. To solve this problem, a new method of fault section location based on intrinsic mode function (IMF) energy moments and least squares support vector machines (LS-SVM) was proposed. Firstly, the fault current signals were decomposed into several EVIFs based on ensemble empirical mode decomposition (EEMD), then the fault feature vectors of IMF energy moments were obtained by integrating the IMF components with time. Secondly, the IMF energy moments with high correlation coefficients were taken as learning samples, then they were inputted to LS-SVM classifier to obtain fault selection location model. Finally, the unknown fault samples were inputted to the LS-SVM classifier trained before to achieve fault section location results. The simulation results show that this method can recognize features of fault signals accurately and identify the fault sections correctly. © 2015 IEEE.

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