Artificial intelligence power grid parameter generation and identification method based on polynomial mixed measurement model

Jun Gu; Liang Cao; Xuan Wang; Jianxin Zhang; Yong Cui; Suirong Li

doi:10.1117/12.2686682

6 August 2023 Artificial intelligence power grid parameter generation and identification method based on polynomial mixed measurement model

Jun Gu, Liang Cao, Xuan Wang, Jianxin Zhang, Yong Cui, Suirong Li

Author Affiliations +

Proceedings Volume 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023); 1278106 (2023) https://doi.org/10.1117/12.2686682
Event: 2023 International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 2023, Guangzhou, JS, China

Abstract

The accuracy and time variation of component parameters in the power grid model affect the real power grid state for accurate simulation analysis and calculation, the use of the power grid operation hybrid measurement data of the source of online data, rectangular coordinates is adopted to establish the unified mixed measurement model, save more intermediate variable step, by using the multivariate polynomial model directly. The power grid branch model is used to construct the artificial intelligence decision variables, and the optimization multi-objective function is constructed, and the differential evolution algorithm is selected to optimize the solution, and finally realize the parameter identification and bad data detection. An example of standard model shows that the algorithm is effective for parameter identification.

Citation Download Citation

Jun Gu, Liang Cao, Xuan Wang, Jianxin Zhang, Yong Cui, and Suirong Li "Artificial intelligence power grid parameter generation and identification method based on polynomial mixed measurement model", Proc. SPIE 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 1278106 (6 August 2023); https://doi.org/10.1117/12.2686682

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