李悦蕾, 张放, 申洪明, 王紫琪. 基于同步相量频谱拟合的电力系统次/超同步振荡的动态参数辨识[J]. 中国电机工程学报, 2025, 45(2): 551-564. DOI: 10.13334/j.0258-8013.pcsee.230694
引用本文: 李悦蕾, 张放, 申洪明, 王紫琪. 基于同步相量频谱拟合的电力系统次/超同步振荡的动态参数辨识[J]. 中国电机工程学报, 2025, 45(2): 551-564. DOI: 10.13334/j.0258-8013.pcsee.230694
LI Yuelei, ZHANG Fang, SHEN Hongming, WANG Ziqi. Dynamic Parameter Identification With Synchronous Spectrum Fitting Technique for Sub/Supersynchronous Oscillations in Power Systems[J]. Proceedings of the CSEE, 2025, 45(2): 551-564. DOI: 10.13334/j.0258-8013.pcsee.230694
Citation: LI Yuelei, ZHANG Fang, SHEN Hongming, WANG Ziqi. Dynamic Parameter Identification With Synchronous Spectrum Fitting Technique for Sub/Supersynchronous Oscillations in Power Systems[J]. Proceedings of the CSEE, 2025, 45(2): 551-564. DOI: 10.13334/j.0258-8013.pcsee.230694

基于同步相量频谱拟合的电力系统次/超同步振荡的动态参数辨识

Dynamic Parameter Identification With Synchronous Spectrum Fitting Technique for Sub/Supersynchronous Oscillations in Power Systems

  • 摘要: “双高”新型电力系统中的次同步振荡主要由风电、光伏的电力电子设备谐振引起,且可能伴随与之频率耦合的超同步振荡,振荡传播广且变化快,因此有必要对其进行同步动态监测。该文提出一种基于同步相量频谱拟合的电力系统次/超同步振荡动态参数辨识方法。通过构建矩阵形式的方程组还原同步相量频谱的叠加特性,可准确辨识频移基波、次/超同步分量的频率、幅值和相位。该文算法相比于现有算法的优势在于,大幅将频谱分析方法必要的时间窗长缩短至200 ms,实现利用基波同步相量的次同步振荡百毫秒量级的动态同步监测;实现了同步相量数据丢失下的参数辨识;以频谱幅值作为误差权重,提升了参数辨识结果的精度。基于模拟和仿真的同步相量数据的算例结果表明,受噪声影响且数据丢失条件下,该文所提方法仍可行有效。

     

    Abstract: Subsynchronous oscillation in power systems with high penetration of renewables and inverters is mainly caused by the resonance of the power electronic equipment of wind power and photovoltaic, which may be accompanied by frequency coupling of supersynchronous oscillation. Oscillations spread widely and change quickly. Therefore, synchronous dynamic monitoring of subsynchronous oscillation is required. In this paper, a dynamic parameter identification method of power system sub/supersynchronous oscillations based on synchronous phasor spectrum fitting is proposed. By constructing a system of equations in matrix form, the superposition characteristics of the synchronous phasor spectrum are restored. It can accurately obtain the frequency, amplitude and phase of the basic component of the frequency shift and the sub/supersynchronous components. This algorithm has great advantages over the existing algorithms. First, the algorithm significantly reduces the necessary time window for spectrum analysis methods to 200 ms. It can achieve dynamic synchronous monitoring of sub synchronous oscillations in the order of one hundred milliseconds using fundamental synchronous phasors. Then, the problem of the influence of synchronous phasor data loss on parameter identification is solved. Finally, this algorithm uses spectral amplitude as error weight to improve the accuracy of parameter identification results. The results of simulated synchronous phasor data and actual simulation data show that the proposed method is still effective even under the condition of noise and data loss.

     

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