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随机数据驱动的电力系统小干扰稳定在线评估方法
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东北电力大学电气工程学院,吉林省吉林市 132012

摘要:

系统自然激励下的随机响应数据中蕴含丰富的机电行为特征信息,准确地从随机响应数据中辨识小干扰稳定特征参数对于指导电力系统安全稳定运行具有重要现实意义。文中提出了随机数据驱动下基于子空间最优模式分解的小干扰稳定特征参数在线辨识算法。该算法通过对输入数据进行基于正交投影的矩阵线性变换得到其奇异子阵,并利用共轭梯度算法迭代求解最佳低维正交空间,以实现奇异子阵之间高维映射矩阵的最优低维近似,根据最优低维映射矩阵的特征值分解结果可以准确获得系统小干扰稳定特征参数,即振荡频率、阻尼比、模态。基于正交投影的矩阵线性变换以及共轭梯度法的引入使得动态模式分解法能较好地适应随机响应数据。IEEE 16机68节点系统和实际系统量测数据的计算和分析验证了所提算法的有效性和准确性。

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作者简介:

周书宇(1995—),男,博士研究生,主要研究方向:电力系统稳定与控制。E-mail:zzhousy@163.com
蔡国伟(1968—),男,通信作者,教授,博士生导师,主要研究方向:含新能源并网的电力系统稳定与控制。E-mail:cai4806439@126.com
杨德友(1983—),男,教授,博士生导师,主要研究方向:电力系统分析、稳定与控制。


Ambient Data-driven On-line Evaluation Method of Power System Small Signal Stability
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Affiliation:

School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Abstract:

The ambient data under the natural excitation of the system contains rich electromechanical characteristic information. Accurate identification of small signal stability characteristic parameters from ambient data is of great practical significance for guiding the safe and stable operation of the power system. An on-line identification algorithm of small signal stability characteristic parameters driven by ambient data based on subspace optimal mode decomposition (Sub-OpMD) is proposed. The algorithm obtains the singular sub-matrix by the matrix linear transformation of the input data based on the orthogonal projection, and the conjugate gradient algorithm is used to iteratively solve the optimal low-dimensional orthogonal space to achieve the optimal low-dimensional approximation of the high-dimensional mapping matrix between singular sub-matrices. According to the eigenvalue decomposition result of the optimal low-dimensional matrix, the small signal stability characteristic parameters of the system can be accurately obtained, i.e., the oscillation frequency, damping ratio and mode. The matrix linear transformation based on orthogonal projection and the introduction of conjugate gradient method make the dynamic mode decomposition (DMD) method better adapt to the ambient data. The calculation and analysis of IEEE 16-generator 68-bus system and actual system measured data verify the effectiveness and accuracy of the proposed algorithm.

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引用本文
[1]周书宇,蔡国伟,杨德友,等.随机数据驱动的电力系统小干扰稳定在线评估方法[J].电力系统自动化,2022,46(1):94-100. DOI:10.7500/AEPS20210630003.
ZHOU Shuyu, CAI Guowei, YANG Deyou, et al. Ambient Data-driven On-line Evaluation Method of Power System Small Signal Stability[J]. Automation of Electric Power Systems, 2022, 46(1):94-100. DOI:10.7500/AEPS20210630003.
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  • 收稿日期:2021-06-30
  • 最后修改日期:2021-08-19
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  • 在线发布日期: 2022-01-05
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