Disturbance Location Method of Power System Based on Over-Complete Reconstruction Dictionary Design
Yu Huanan, Li Yongxin, Wang He
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China
Abstract：Disturbance location of power system quickly and accurately is an important precondition to implement safety and stability control measures of power system. The disturbance source has sparse characteristics in the spatial distribution since the disturbance will not occur in a large range at the same time. How to use the limited number of phasor measurement unit devices to perceive the disturbance in the power system is an important research topic. This paper presented a power system disturbance positioning based on a overcomplete dictionary design, its innovation is based on the changes of the system topology or electrical parameters of the disturbance to construct a overcomplete dictionary, in combination with compressed sensing reconstruction algorithm to recover from low dimensional phasor measurement unit observation data of the sparse characteristics to high dimensional disturbance location data, then the disturbance of power system got located. The generator shedding and the load shedding disturbance are taken as examples. A large number of simulation results show that the method proposed in this paper can locate the disturbance source with high accuracy, and it has important reference significance for other types of disturbance location.
于华楠, 李永鑫, 王鹤. 基于过完备字典设计的电力系统扰动定位方法[J]. 电工技术学报, 2020, 35(7): 1444-1453.
Yu Huanan, Li Yongxin, Wang He. Disturbance Location Method of Power System Based on Over-Complete Reconstruction Dictionary Design. Transactions of China Electrotechnical Society, 2020, 35(7): 1444-1453.
[1] 韩学军, 石磊, 赵凤英. 连锁过载识别方法的比较[J]. 电网技术, 2007, 31(21): 37-40, 60. Han Xuejun, Shi Lei, Zhao Fengying.Comparative research on identifying method of cascading overload[J]. Power System Technology, 2007, 31(21): 37-40, 60. [2] 陈彬, 于继来. 基于电网潮流分布特征的在线故障智能诊断[J]. 电力系统自动化, 2007, 31(16): 29-34. Chen Bin, Yu Jilai.Online fault intelligent diagnosis based on distributed characteristics of power flow with outage of electric power network[J]. Automation of Electric Power Systems, 2007, 31(16): 29-34. [3] 金涛, 陈毅阳, 段小华, 等. 基于改进DFT的电力系统同步相量测量算法研究[J]. 电工技术学报, 2017, 32(17): 1-10. Jin Tao, Chen Yiyang, Duan Xiaohua, et al.Research on synchronous phasor measurement algorithm of power system based on improved DFT[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 1-10. [4] 罗深增, 李银红, 游昊, 等. 基于同步相量测量单元的串联补偿线路自适应故障定位算法[J]. 电工技术学报, 2017, 32(5): 143-151. Luo Shenzeng, Li Yinhong, You Hao, et al.An adaptive fault location algorithm based on PMU measurement for series compensated transmission lines[J]. Transactions of China Electrotechnical Society, 2017, 32(5): 143-151. [5] 陈雷, 郑德忠, 廖文喆. 基于压缩感知的含扰动电能质量信号压缩重构方法[J]. 电工技术学报, 2016, 31(8): 163-171. Chen Lei, Zheng Dezhong, Liao Wenzhe.Method based on compressed sensing for compression and reconstruction of power quality signals with disturbances[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 163-171. [6] 刘嫣, 汤伟, 刘宝泉. 基于压缩感知的电能质量扰动数据稀疏分析与改进重构算法[J]. 电工技术学报, 2018, 33(15): 3461-3470. Liu Yan, Tang Wei, Liu Baoquan.Data sparse analysis and improved reconstruction algorithm of power quality disturbance based on compressed sensing[J]. Transactions of China Electrotechnical Society, 2018, 33(15): 3461-3470. [7] 秦晓辉, 毕天姝, 杨奇逊. 基于广域同步量测的电力系统扰动识别与定位方法[J]. 电网技术, 2009, 33(12): 35-41. Qin Xiaohui, Bi Tianshu, Yang Qixun.WAMS based power system disturbance identification and location approach[J]. Power System Technology, 2009, 33(12): 35-41. [8] 徐彪, 尹项根, 张哲, 等. 矩阵算法和优化算法相结合的配电网故障定位[J]. 电力系统自动化, 2019, 43(05): 152-161. Xu Biao, Yin Xianggen, Zhang Zhe, et al.Fault location for distribution network based on matrix algorithm and optimization algorithm[J]. Automation of Electric Power Systems, 2019, 43(05): 152-161. [9] Majidi M, Etezadi-Amoli M, Fadali M S.A sparse-data-driven approach for fault location in transmission networks[J]. IEEE Transactions on Smart Grid, 2017, 8(2): 548-556. [10] 于华楠, 马聪聪, 王鹤. 基于压缩感知估计行波自然频率的输电线路故障定位方法研究[J]. 电工技术学报, 2017, 32(23): 140-148. Yu Huanan, Ma Congcong, Wang He.Transmission line fault location method based on compressed sensing estimation of traveling wave natural frequencies[J]. Transactions of China Electrotechnical Society, 2017, 32(23): 140-148. [11] Zhu Hao, Giannakis G B.Sparse overcomplete representations for efficient identification of power line outages[J]. IEEE Transactions on Power Systems, 2012, 27(4): 2215-2224. [12] 齐郑, 庄舒仪, 刘自发, 等. 基于并联电阻扰动信号的配电网故障定位方法分析[J]. 电力系统自动化, 2018, 42(09): 195-200. Qi Zheng, Zhuang Shuyi, Liu Zifa, et al.Analysis on distribution network fault location method based on parallel resistance disturbed signal injection[J]. Automation of Electric Power Systems, 2018, 42(09): 195-200. [13] Xu Yan, Liu Jingyan.Reasearch of fault line selection and fault type recognition method of DC system based on graph theory[J]. Protection and Control of Modern Power Systems, 2018, 39(8): 2279-2286. [14] Denegri C B, Invernizzi M, Serra P.Perturbation identification via voltage phasor monitoring in transmission systems[C]//2003 IEEE Bologna Power Tech Conference Proceedings, Bologna, Italy, 2004: 23-26. [15] 马静, 李金龙, 杨奇逊. 基于故障模型的广域故障定位新方法[J]. 电力系统保护与控制, 2010, 38(20): 74-78. Ma Jing, Li Jinlong, Yang Qixun.A novel wide-area fault location algorithm based on fault model[J]. Power System Protection and Control, 2010, 38(20): 74-78. [16] 徐慧明, 毕天姝. 计及暂态过程的多支路切除潮流转移识别算法研究[J]. 中国电机工程学报, 2007, 27(16): 24-30. Xu Huiming, Bi Tianshu.Flow transferring identification algorithm for multi-branches removal event with consideration of transient phenomena[J]. Proceedings of the CSEE, 2007, 27(16): 24-30. [17] 国家电网有限公司. Q/GDW10131-2017电力系统实时动态监测系统技术规范[S]. 2017. [18] Aminifar F, Khodaei A, Fotuhi-Firuzabad M A. Contingency-constrained PMU placement in power networks[J]. IEEE Transactions on Power System, 2010, 25(1): 516-523.
2020, Vol. 35 
