Prof. Xi Wu Profile

Prof. Xi Wu

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Proceedings Article | 25 September 2023 Paper

On-line estimation method of sub-synchronous oscillation parameters based on environmental incentive data

Xi Wu, Peng Wang, Qiang Li, Bixing Ren, Ke Xu

Proceedings Volume 12788, 127884J (2023) https://doi.org/10.1117/12.3004283

KEYWORDS: Data modeling, Education and training, Wind energy, Performance modeling, Artificial intelligence, Neural networks, Mathematical modeling, Evolutionary algorithms, Computer simulations, Systems modeling

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The phenomenon of sub-synchronous oscillation is an important safety hazard in electric power system. Although the subsynchronous oscillation (SSO) parameter identification method based on transient disturbance data has excellent identification effect, it cannot be used in the risk perception task of sub-synchronous oscillation. The environmental incentive data in the steady state of the electric power system contains a wealth of dynamic information. If the estimation of sub-synchronous oscillation parameters can be realized based on environmental incentive data, not only the risk prediction before sub-synchronous oscillation can be realized, but also the risk analysis of sub-synchronous oscillation under steady state of power system can be realized. The environmental incentive signal is a kind of time series signal, and the information contained in it has strong nonlinear characteristics. In this paper, The method builds an artificial intelligence model based on GRU suitable for dealing with nonlinear time series problems, and introduces Attention mechanism into the model to further improve the model. The method in this paper can extract sub-synchronous oscillation parameter information from environmental incentive data in a variety of complex and changeable power system operation scenarios, so the method in this paper has excellent generalization usability. In addition, the calculation speed of the model is fast, and it also has good real-time performance, which is suitable for online sub-synchronous oscillation parameter estimation; at the same time, the model is driven by data, which avoids the process of mathematical analysis of the model and has the convenience of model-free analysis.

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