Polynomial approximation of the small-signal stability region boundaries and its credible region in high-dimensional parameter space

Su, Yang; Liu, Feng; De, Zhang; Mei, Shengwei

doi:10.1002/etep.1624

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…This equation can be converted into the following one related to the real and imaginary parts of the system eigenvalues ( = α + jβ). Furthermore, because the problem of SSO is related to the Hopf bifurcation (HB), we are only interested in the component of SSSR boundaries composed by the HB points in this paper [24]. At the HB point, at least a pair of eigenvalues has zero real parts, so the equation can be expressed as follows:…”

Section: The Impact Of Reactive Power Output Of Wind Farm On Ssomentioning

confidence: 99%

“…Assume that u x is the eigenvector of J ( μ ) corresponding to the eigenvalue λ :

bold-italicJ false(bold-italicμ false) u_{x} = λ u_{x}

This equation can be converted into the following one related to the real and imaginary parts of the system eigenvalues ( λ = α + jβ ). Furthermore, because the problem of SSO is related to the Hopf bifurcation (HB), we are only interested in the component of SSSR boundaries composed by the HB points in this paper [24]. At the HB point, at least a pair of eigenvalues has zero real parts, so the equation can be expressed as follows:

bold-italicJ false(bold-italicμ false) u_{x} = j β u_{x}

By defining the normalisation equation u x = u xR + j u xI , Equation (8) can be characterised by

({, \begin{matrix} bold-italicJ false(bold-italicμ false) u_{x normalR} + β u_{x normalI} = 0 \\ bold-italicJ false(bold-italicμ false) u_{x normalI} - β u_{x normalR} = 0 \end{matrix})

The feature vectors can be normalised using the following equation: u x T u x = 1.…”

Section: Impact Of Power Output Of Wind Farm On Ssomentioning

confidence: 99%

See 1 more Smart Citation

Low‐cost control strategy based on reactive power regulation of DFIG‐based wind farm for SSO suppression

Guan

Xiang

et al. 2018

IET Renewable Power Generation

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For doubly fed induction generator (DFIG)-based wind farms, the occurrence of sub-synchronous oscillation (SSO) is detrimental to the system operation and should be suppressed promptly. A low-cost control strategy based on reactive power regulation of DFIG-based wind farm is proposed to suppress SSO. First, the influence of the reactive power output of wind farms on SSO is studied in the entire operating region of DFIGs, and several uncertainty factors are taken into account. Furthermore, a reactive power regulation-based control strategy is proposed to suppress SSO by changing the operating point of the system. In addition, a PASTd (Projection Approximation Subspace Tracking based on the Deflation Technique)-based identification method is utilised to realise the online SSO monitoring. The implementation of the proposed control strategy is low cost since it does not require any additional supplementary equipment. The time-domain simulation results have also verified the effectiveness and strong robustness of the proposed control strategy.

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Section: The Impact Of Reactive Power Output Of Wind Farm On Ssomentioning

confidence: 99%

“…Assume that u x is the eigenvector of J ( μ ) corresponding to the eigenvalue λ :

bold-italicJ false(bold-italicμ false) u_{x} = λ u_{x}

bold-italicJ false(bold-italicμ false) u_{x} = j β u_{x}

By defining the normalisation equation u x = u xR + j u xI , Equation (8) can be characterised by

({, \begin{matrix} bold-italicJ false(bold-italicμ false) u_{x normalR} + β u_{x normalI} = 0 \\ bold-italicJ false(bold-italicμ false) u_{x normalI} - β u_{x normalR} = 0 \end{matrix})

The feature vectors can be normalised using the following equation: u x T u x = 1.…”

Section: Impact Of Power Output Of Wind Farm On Ssomentioning

confidence: 99%

Low‐cost control strategy based on reactive power regulation of DFIG‐based wind farm for SSO suppression

Guan

Xiang

et al. 2018

IET Renewable Power Generation

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show abstract

“…Sun and Yu [18] proposed fitting a boundary consisting of Hopf bifurcations using hyper-planes. Based on an implicit function, Yang et al [19] presented a method to obtain the small signal stability region boundary through polynomial approximation. Jia et al [20,21], and Li et al [22] studied the influences on the small signal stability region boundary of exciter voltage limits, time delays and saturated links, respectively.…”

Section: Introductionmentioning

confidence: 99%

Research on a Small Signal Stability Region Boundary Model of the Interconnected Power System with Large-Scale Wind Power

Liu¹,

Ge²,

Lv³

et al. 2015

Energies

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Abstract:For the interconnected power system with large-scale wind power, the problem of the small signal stability has become the bottleneck of restricting the sending-out of wind power as well as the security and stability of the whole power system. Around this issue, this paper establishes a small signal stability region boundary model of the interconnected power system with large-scale wind power based on catastrophe theory, providing a new method for analyzing the small signal stability. Firstly, we analyzed the typical characteristics and the mathematic model of the interconnected power system with wind power and pointed out that conventional methods can't directly identify the topological properties of small signal stability region boundaries. For this problem, adopting catastrophe theory, we established a small signal stability region boundary model of the interconnected power system with large-scale wind power in two-dimensional power injection space and extended it to multiple dimensions to obtain the boundary model in multidimensional power injection space. Thirdly, we analyzed qualitatively the topological property's changes of the small signal stability region boundary caused by large-scale wind power integration. Finally, we built simulation models by DIgSILENT/PowerFactory software and the final simulation results verified the correctness and effectiveness of the proposed model.

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“…In [8], first-and second-order approximations of the smallsignal stability boundary are presented. The authors of [8] use the implicit function theorem to express the relationship between the parameters on the stability boundary.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [8] use the implicit function theorem to express the relationship between the parameters on the stability boundary.…”

Section: Introductionmentioning

confidence: 99%

Approximating the parameter-space stability boundary considering post-contingency corrective controls

Perninge

2015

Electric Power Systems Research

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Polynomial approximation of the small-signal stability region boundaries and its credible region in high-dimensional parameter space

Cited by 11 publications

References 10 publications

Low‐cost control strategy based on reactive power regulation of DFIG‐based wind farm for SSO suppression

Low‐cost control strategy based on reactive power regulation of DFIG‐based wind farm for SSO suppression

Research on a Small Signal Stability Region Boundary Model of the Interconnected Power System with Large-Scale Wind Power

Approximating the parameter-space stability boundary considering post-contingency corrective controls

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