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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Research Article

Distributed Dynamic State Estimation of Power Systems Considering Dynamic State Constraints based on Phasor Measurements Units

In Press, (this is not the final "Version of Record"). Available online 04 January, 2024
Author(s): Li Juan, Wang Jia-ming, Jiang Yun-long, Yang Xiong, Zhu Mao-lin* and Liu Hao
Published on: 04 January, 2024

DOI: 10.2174/0123520965269387231120120044

Price: $95

Abstract

Background: Dynamic state estimation can provide detailed information about power systems. However, there is no clear, dynamic equation for the state transition of bus voltages in power systems. Currently, smoothing methods based on historical data are commonly used, but they cannot ensure accurate state prediction in power systems with a large amount of renewable energy. Moreover, the fast sampling rate of phasor measurement units generates a vast amount of real-time data for the dispatch center, making it challenging for centralized state estimation to meet real-time demands.

Objective: This paper proposes a distributed power system state estimation considering dynamic state constraints to address the above issues.

Methods: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately.

Results: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. The proposed method is compared with other algorithms to prove its superiority.

Conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions.


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