基于稀疏增强动态解耦的电力系统振荡模式与模态辨识方法  

Sparsity Promoting Dynamic Mode Decomposition Based Dominant Modes and Mode Shapes Estimation in Bulk Power Grid

作　　者：李雪[1] 于洋[1] 姜涛[1] 李国庆[1] 刘春晓 Li Xue;Yu Yang;Jiang Tao;Li Guoqing;Liu Chunxiao(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education Northeast Electric Power University,Jilin 132012 China;Power Dispatching and Control Center China Southern Power Grid,Guangzhou 510623 China)

机构地区：[1]现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林132012 [2]南方电网电力调度控制中心,广州510623

出　　处：《电工技术学报》2021年第13期2832-2843,共12页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(51607034,51677023);中国南方电网有限责任公司科技项目(ZDKJXM20180151);国家重点研发计划(2016YFB0900900)资助项目。

摘　　要：提出一种基于稀疏增强动态解耦(SPDMD)的电力系统主导振荡模式及模态评估方法。该方法首先从电力系统的多通道广域量测信息中辨识出可表征系统关键动态振荡特征信息的低阶状态矩阵;然后,基于该低阶状态矩阵,借助交替方向乘子(ADMM)和拉格朗日乘子(LM)估计各振荡模式的最优振幅系数,根据系统主导振荡模式的最优振幅系数不为0这一特点,从低价状态矩阵中精确筛选出系统的主导振荡模式及模态;最后,将该文所提方法应用到16机68节点测试系统和中国南方电网进行分析,有效验证了所提方法的正确性与实用性。This paper proposes a sparsity promoting dynamic mode decomposition(SPDMD)approach for dominant modes and mode shapes assessment in bulk power grid by using the wide area measurement.The SPDMD is first employed to estimate the low-order state matrix containing the critical dynamic oscillation features from the multichannel wide area measurements.Then,the alternating direction multiplier method(ADMM)and Lagrangian multiplier(LM)are used to estimate the optimized amplitude coefficients of the oscillation modes embedded in the low-order state matrix.Further,using the optimized amplitude coefficients,the dominant modes and mode shapes are separated.Finally,the proposed approach was evaluated by the 16-machine 68-bus test system as well as China Southern Power Grid,the results confirm the accuracy and effectively of the proposed SPDMD in dominant modes and mode shapes.

关 键 词：广域量测信息 稀疏增强动态解耦 主导振荡模式 主导振荡模态 

分 类 号：TM712[电气工程—电力系统及自动化]