Tao Xianghong
Electric Power Research Institute
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Featured researches published by Tao Xianghong.
ieee international conference on power system technology | 2014
Wang Hongfu; Tao Xianghong; Zhang Zhiqiang; Gao Chong; Yu Hao; Mu Shixia
DC power flow is widely used in power system analysis such as power flow adjusting and fast security assessment, which mainly attributes to its advantages of linearity and rapidity. The active power balance problem caused by the network loss in DC power flow algorithm is researched in this paper. An improved DC power flow algorithm is proposed to reduce the power flow error which is mainly because of the neglect of the active power loss in transmission lines and transformers. In this improved DC power flow algorithm, the active power loss of each branch in the network is simulated with equivalent loads located at both ends of the branch, and so the power injection at each node contains an extra virtual equivalent load in order to balance the active power loss produced by the connected branches. Using this equivalent load model, one of the most critical problems is to calculate the equivalent loads added to the power grid as accurately as possible. If convergent initial power flow results calculated by AC power flow are available, the equivalent loads can be calculated directly and accurately. But in some cases, it is not convenient to obtain a convergent power flow especially when the topology or power injection of power grid model is changed greatly. In order to apply this equivalent load model without dependence on convergent initial power flow, a new effective iteration method is also presented to calculate the equivalent loads. The improved DC power flow algorithm proposed in this paper preserves the linearity and rapidity of standard DC power flow algorithm, but significantly achieves an advantage of much higher calculation accuracy. Plenty of actual calculation examples show that the improved DC power flow algorithm is effective and practical, as the power flow result calculated by the improved algorithm is much closer to the actual value than by the standard algorithm.
Archive | 2013
Wang Hongfu; Wang Yi; Dong Yifeng; Liu Daowei; Ma Shiying; Tang Yong; Hou Junxian; Tao Xianghong; Fan Ya Na; Zhi Zhi; Zhang Zhiqiang; Li Xiaobin
Archive | 2017
Dong Yifeng; Hou Junxian; Wang Yi; Wang Hongfu; Zhong Wuzhi; Tang Yong; Liu Tao; Wan Lei; Tao Xianghong; Lu Jun; Feng Jing
Archive | 2016
Liu Xiaoming; Niu Xinsheng; Wang Hongfu; Ma Shiying; Hou Junxian; Tao Xianghong; Xiao Jing; Liu Yanjia; Fan Yana; Zhi Zhi; Zhang Zhiqiang; Zhang Jie; Yang Bin; Zhang Tianbao; Wang Yuan; An Peng
Archive | 2016
Wang Yi; Wang Hongfu; Dong Yifeng; Hou Dunxian; Tao Xianghong; Shao Baozhu; Zhong Wuzhi; Tang Yong; Song Xinli; Liu Daowei; Liu Tao; Ye Xiaohui; Wang Tiezhu; Zhang Zhiqiang; Liu Yanjia
Archive | 2016
Yang Xuetao; Cao Shujiang; Gao Zeming; Cheng Lun; Wang Ruixin; Zhi Zhi; Song Dunwen; Ma Shiying; Wang Hongfu; Tao Xianghong; Zhang Haishun; Xiong Xuanwen; Wei Shiquan; Hou Junxian; Wang Yi
Archive | 2016
Wang Hongfu; Ma Shiying; Hou Junxian; Tao Xianghong; Xiao Jing; Liu Yanjia; Fan Yana; Zhi Zhi; Zhang Zhiqiang; Sun Yiqian; Yu Yongjun; Qi Xiaoxiao; Qin Yanhui; Wang Fangnan; Zheng Shaopeng
Archive | 2015
Wang Yi; Wang Hongfu; Dong Yifeng; Hou Junxian; Tao Xianghong; Tang Yong; Zhong Wuzhi; Song Dunwen; Wan Lei; Du Sanen; Xu Pengfei; Feng Jing; Liu Tao; Liu Daowei; Yang Xuetao; Zhang Zhiqiang; Liu Yanjia
Archive | 2014
Tao Xianghong; Wang Hongfu; Hou Junxian; Ma Shiying; Xiao Jing; Fan Ya Na; Zhi Zhi; Zhang Zhiqiang; Liu Yanjia; Wang Peng; Wang Dongxiao; Zheng Zhongfei; Zhao Wei; Kong Peng
Archive | 2014
Wang Hongfu; Tao Xianghong; Song Dunwen; Hou Junxian; Ma Shiying; Tian Bei; Li Feng; Zhou Jianli; Fan Ya Na; Zhi Zhi; Zhang Zhiqiang; Liu Yanjia; Wang Peng; Xiao Jing; Zhang Haishun; Xiong Xuanwen; Yang Xuetao; Wei Shiquan; Feng Jing