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Event-Triggered Data-Driven Distributed LFC Using Controller-Dynamic-Linearization Method

delete2025-01-01
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PRE
AI
X
Xuhui Bu *
张彦 封面图
张彦 (Yan Zhang)
Y
Yiming Zeng
Z
Zhongsheng Hou
DOI:10.1109/TSIPN.2025.3525950delete
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摘要

摘要

En 中文
This paper is concerned with an event-triggered distributed load frequency control method for multi-area interconnected power systems. Firstly, because of high dimension, nonlinearity and uncertainty of the power system, the relevant model information cannot be fully obtained. To realize the design of LFC algorithm under the condition that the model information is unknown, the equivalent functional relationship between the control signal and the area-control-error signal is established by using a dynamic linearization technique. Secondly, a novel distributed load frequency control algorithm is proposed based on controller dynamic-linearization method and the controller parameters are tuned online by constructing a radial basis function neural network. In addition, to reduce the computation and communication burden on the system, an event-triggered mechanism is also designed, in which whether the data is transmitted at the current instant is completely determined by a triggering condition. Rigorous analysis shows that the proposed method can render the frequency deviation of the power system to converge to a bounded value. Finally, simulation results in a four-area power system verify the effectiveness of the proposed algorithm.
Keyword:
Power systems
Power system stability
Power system dynamics
Frequency control
Mathematical models
Event detection
Information processing
Load modeling
Nonlinear dynamical systems
Uncertainty
Multi-area power systems
load frequency control
distributed controller-dynamic-linearization method
event-triggered strategy
data-driven

期刊

IEEE Transactions on Signal and Information Processing over Networks 封面图
IEEE Transactions on Signal and Information Processing over Networks
IF:
4.9
论文数:
736
被引数:
1.9K

机构

H
henan polytechnic university
学者数:
1.2W
论文数: 7.2K
被引数: 5
Q
Qingdao University
学者数:
3.1W
论文数: 2.1W
被引数: 3.7W
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