Dynamic State Estimation for Synchronous Generator with Communication Constraints: An Improved Regularized Particle Filter Approach

Accurate acquisition of real-time electromechanical dynamic states of synchronous generators plays an essential role in power systems. The phasor measurement units (PMUs) are widely used in data acquisition of synchronous generator operation parameters, which can capture the dynamic responses of gen...

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Bibliographic Details
Published in:IEEE transactions on sustainable computing 2023-04, Vol.8 (2), p.1-10
Main Authors: Bai, Xingzhen, Qin, Feiyu, Ge, Leijiao, Zeng, Lin, Zheng, Xinlei
Format: Article
Language:English
Subjects:
Algorithms
Communication
Data acquisition
Data transmission
Dynamic state estimation
event-triggering scheme
Generators
Genetic algorithms
Information centers
Mathematical models
Measuring instruments
Parameters
Particle filters
Particle measurements
Phasor measurement units
Phasors
Power system dynamics
regularized particle filter
Resource utilization
State estimation
Synchronous generators
Synchronous machines
wide-area measurement system (WAMS)
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Summary:Accurate acquisition of real-time electromechanical dynamic states of synchronous generators plays an essential role in power systems. The phasor measurement units (PMUs) are widely used in data acquisition of synchronous generator operation parameters, which can capture the dynamic responses of generators. However, distortion of measurement results of synchronous generator operation parameters is inevitable due to various reasons, such as device failure and operating environment interference and so on. Meanwhile, it is hard to transmit gigantic volumes of data to the information center due to limited communication bandwidth. To tackle these challenges, this paper proposes a dynamic state estimation method for synchronous generators with event-triggered scheme. The proposed method first establishes a non-linear model to describe the dynamics of generators. Then, a measure-based event-triggering scheme is adopted to schedule the data transmission from the sensor to estimator, thus reducing communication pressure and enhanced resource utilization. Finally, an improved regularized particle filter (IRPF) algorithm is designed to guarantee the estimation performance. To this end, the genetic algorithm is used to optimize the particles sampled by regularized particle filter algorithm, which can solve particle exhaustion problem. The CEPRI7 system is used to verify the performance of the proposed method.
ISSN:2377-3782
2377-3790
DOI:10.1109/TSUSC.2022.3221090