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

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-06 DOI:10.1109/TSIPN.2025.3525950

Xuhui Bu;Yan Zhang;Yiming Zeng;Zhongsheng Hou

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Abstract

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.

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基于控制器动态线性化方法的事件触发数据驱动分布式LFC

本文研究了一种多区域互联电力系统的事件触发分布式负荷频率控制方法。首先，由于电力系统的高维、非线性和不确定性，不能充分获取相关的模型信息。为了实现模型信息未知情况下LFC算法的设计，采用动态线性化技术建立了控制信号与面积控制误差信号之间的等效函数关系。其次，提出了一种基于控制器动态线性化的分布式负荷频率控制算法，并通过构建径向基函数神经网络对控制器参数进行在线整定。此外，为了减少系统的计算和通信负担，还设计了事件触发机制，在该机制中，数据是否在当前时刻传输完全由触发条件决定。严格的分析表明，该方法能使电力系统的频率偏差收敛到一个有界值。最后，对四区电力系统进行了仿真，验证了算法的有效性。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.