On the Security and Stability Analysis of Power System Based on Big Data Mode

Wei, Da Qian; Wang, Bo; Ji, Xingpei

doi:10.4028/www.scientific.net/amr.791-793.1854

Cited by 2 publications

(1 citation statement)

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“…Reference [6] uses the electrical distance method to group the system based on grid parameters. Reference [7] proposes a grouping method based on state space analysis. Reference [8] uses the Epsilon decomposition method to identify coherent groups.…”

Section: Introductionmentioning

confidence: 99%

An Improved Slow Coherent Groups Recognition Method for Dynamic Equivalence of Large Power System

Huang²,

Zhao³

et al. 2023

J. Phys.: Conf. Ser.

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The slow coherent groups recognition method is one of the dynamic equivalence techniques, which is based on the singular perturbation principle. Its algorithm performance is widely recognized. In order to reduce the solving difficulty and time consuming, this method ignores the generator stator damping effect. But during a fault, the generator stator damping effect directly affects the oscillation amplitude and frequency of the rotor angular curve. If the damping characteristics of the original system are ignored, when the damping torque coefficients of each generator vary greatly, the coherence between generators will be affected, and the accuracy of the simplified equivalent system will inevitably decrease. This paper proposes a new method based on the traditional slow coherent groups’ recognition method. The new method, which is based on the second-order model of the generator, can consider the generator stator damping effect without increasing the algorithm complexity. The active and reactive power of the system are decoupled. The eigenvalues of the system model and the general solution of the second-order differential equations are obtained through rigorous derivation. It also proposes a two-scale coherent groups recognition criterion. Finally, the effectiveness of the new method is verified based on the IEEE-39-Bus system.

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Section: Introductionmentioning

confidence: 99%