Leng Hua
Electric Power Research Institute
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Publication
Featured researches published by Leng Hua.
international conference on measuring technology and mechatronics automation | 2017
Chen Hong; Liu Haifeng; Leng Hua; Zhu Jiran; Tang Haiguo; Zhang Zhidan
A method to discriminate between magnetizing inrush and internal fault in power transformer is proposed and evaluated in this paper. First, the differential current signal in power transformer is scrutinized and its intrinsic features during magnetizing inrush conditions are introduced. Secondly, an adaptive generalized morphological filter is designed to suppress the noise in differential current signal. At last, the complexity of signal waveform is estimated applying the grille fractal. As the waveform complexity is much bigger in magnetizing inrush than in fault condition, the proposed method can reliably and accurately identify transformer inrush from faults. Extensive simulations validate the merit of this method for various conditions.
international conference on intelligent computation technology and automation | 2017
Leng Hua; Tang Haiguo; Gong Hanyang; Zhang Zhidan
It is difficult to locate the single-phase-earth fault in neutral ineffectively grounded systems, because the fault current is weak. Aimed at this problem, this paper proposes a new fault section location method based on the relative entropy degree. The proposed method computes the relative entropy degree of every feeder section, and then determines the state of the sections. For the faulty section, the relative entropy degree is larger than the preset threshold, due to the significant difference between transient zero-sequence currents in both sides of the section. While, the relative entropy degree in healthy sections is less than the threshold, because of the similarity of transient zero-sequence currents in both sides of the section. Numerous simulation tests are implemented under different operating conditions, which demonstrate the correctness, reliability, and high engineering application value of the proposed method.
international conference on intelligent computation technology and automation | 2017
Leng Hua; Zhu Jiran; Gong Fangliang; Zhang Zhidan
A method for fault location based on correlative characteristic values (CCVs) in resonant grounded system is presented. In this method, zero-sequence current signals of two different data windows are extracted for each feeder terminal unit (FTU) after a single-phase-to-ground fault occurs. Then, their correlation coefficient which is called CCV of each FTU is calculated. Furthermore, CCVs of all FTUs in the faulty feeder are uploaded to control centre. For healthy section, two CCVs of two FTUs at both sides of the section are almost the same while two CCVs of two FTUs, for faulty section, have a great difference. Thus, faulty section can be located. Simulation results under different fault conditions show that the method is effective and feasible.
ieee advanced information technology electronic and automation control conference | 2017
Leng Hua; Zhu Jiran; Tang Haiguo; Zhang Zhidan; Gong Hanyang; Zhong Huabing
In order to solve the fault location problem of single phase grounding fault in non-solidly earthed distribution network, the constitution, principle and application condition of a new fault locating method are presented in this paper. This method is realized by the distribution automation system (DAS), feeder monitoring unit has been deployed to acquire fault zero sequence current waveform and send the waveform to DAS master station. The locating workstation deployed on master station to get intrinsic mode function (IMF) component from zero sequence current waveform by using empirical mode decomposition (EMD) algorithm, then calculated similarity coefficient of IMF component by analyzing waveform similarity algorithm, finally located fault section according to similarity coefficient. The actual operation results verified that the locating method is reasonable and effective.
Archive | 2013
Zhao Yongsheng; Tan Zhihai; Sun Qiupeng; Leng Hua; Zhou Shuxiong; Zhang Liangfeng; Ge Liang; Zhao Fengqing; Zhang Lei; Li Hua
Archive | 2014
Li Xinran; Zhu Liang; Chen Hong; Leng Hua; Li Longgui; Zhu Jiran; Tang Haiguo; Gong Hanyang
Archive | 2014
Qi Mingjun; Kang Taifeng; Leng Hua; Chen Hong; Zhong Huabing; Zhao Fengqing; Zhu Jiran; Tang Haiguo; Gong Hanyang
Archive | 2014
Zhu Jiran; Leng Hua; Tang Haiguo; Xue Wei; Chen Hong; Li Hongqing; Wang Wei; Wang Bi
Archive | 2015
Qi Mingjun; Cheng Wenxin; Leng Hua; Zhu Jiran; Kang Taifeng; Tang Haiguo; Zhong Huabing; Gong Hanyang; Zhao Fengqing
Archive | 2015
Qi Mingjun; Wu Fangrong; Peng Minfang; Zhu Liang; Gong Hanyang; Tang Haiguo; Zhu Jiran; Leng Hua