Wind power output error probability density distribution fitting based on Gaussian mixture function

Yaming Ren

doi:10.1117/12.2684704

21 July 2023 Wind power output error probability density distribution fitting based on Gaussian mixture function

Yaming Ren

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127172C (2023) https://doi.org/10.1117/12.2684704
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

With the increasing scale of wind power, the impact of wind fluctuations on the power network must be considered. Considering the randomness of wind have a serious impact on power grid, we use Gaussian mixture function method to approximate the wind power output error probability density distribution. Specifically, we use Davidson-Boding Index (DBI) to determine the optimal number of cluster center. Then, the fastest gradient descent method is employed to solve the weights of Gaussian function. Finally, the simulation results show that the Gaussian mixture function can effectively approximate the wind power output error probability density distribution.

Citation Download Citation

Yaming Ren "Wind power output error probability density distribution fitting based on Gaussian mixture function", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127172C (21 July 2023); https://doi.org/10.1117/12.2684704

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