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| Cellular Computational Networks Based Hierarchical Data-Driven Dynamic State Estimation Method Considering Uncertainties |
| Lili Wu,Yi Wang, Member, IEEE,Yaoqiang Wang, Senior Member, IEEE,Jikai Si, Member, IEEE |
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| Abstract: |
| Accurate generator information is crucial for the efficient control and operation of a power system. This study proposes a hierarchical data-driven approach for dynamic state estimation (DSE) of generators using cellular computational networks (CCNs) structure. The proposed method initially divides the problem of dynamic state estimation into multiple layers through hierarchical architecture. In the prediction layer, CCNs are employed to reduce the system scale by considering only relevant generators. In the correction layer, a novel adaptive filter is utilized to increase data abundance. Simulation results demonstrate that the proposed hierarchical data-driven method can accurately estimate states using PMU data alone while maintaining high computational efficiency. Additionally, it offers easy scalability and strong robustness against uncertainties. The proposed method has potential applications in online dynamic state estimation and real-time security monitoring. |
| Key words: Cellular computational networks, data
driven, dynamic state estimation, hierarchical, model
uncertainty. |
| DOI：10.23919/PCMP.2023.000307 |
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| Fund:This work is supported in part by the National Natural Science Foundation of China (No. 62203395), in part by the China Postdoctoral Science Foundation (No. 2023TQ0306), in part by the Natural Science Foundation of Henan Province (No.242300421167), and in part by the Postdoctoral Research Project of Henan Province (No. 202101011). |
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