A Modified Data-driven Power Flow Model for Power Estimation with Incomplete Bus Data

Zheng, Xing; Lao, Keng-Weng; Gao, Hongjun; Dai, Ningyi

doi:10.1109/cpeee54404.2022.9738684

Cited by 2 publications

(1 citation statement)

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“…An approach with high adaptability to the nonlinearity of power flow is proposed in 6, which can significantly improve the calculation accuracy. Reference (Xing et al, 2022;Xing et al, 2022) respectively introduce a single bus data-driven power estimation based on modified linear power flow model and a modified data-driven power flow model for power estimation with incomplete bus data. The models proposed in these references exhibit higher accuracy compared to the linear power flow model.…”

Section: Introductionmentioning

confidence: 99%

Research on data-driven power flow calculation method based on undirected-graph delooping-backtracking

Zhu,

Hu,

Liu

et al. 2024

Front. Energy Res.

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As the scale of the power grid expands and distributed energy sources are integrated, along with the emergence of random loads, topological control of distribution networks has become a novel means of control. Therefore, data-driven power flow calculations must be capable of rapidly and accurately computing power flow results even when there are changes in the network’s topology. In this paper, a data-driven power flow calculation method is proposed to take topological changes into account. Based on initial loop data, we employ an undirected-graph delooping-backtracking method to generate a set of feasible topological samples. Using the Monte Carlo method on this basis, we generate feasible samples for the network’s topology and power injection, thereby establishing a training dataset. By training a deep neural network on these samples and adjusting network parameters, we effectively address power flow calculations in the presence of topological changes. Case study results demonstrate that the data-driven power flow calculation method, considering topological changes, can rapidly and accurately compute power flow results when topology alterations occur.

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Section: Introductionmentioning

confidence: 99%