Neural Network Based Data-Driven Load Frequency Control for Interconnected Power Systems

被引：0
作者：
Chen Z. [1 ]
Bu X. [1 ,2 ]
Guo J. [1 ]
机构：
[1] School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo
[2] Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Henan Polytechnic University, Jiaozuo
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 21期
关键词：
data driven control; Interconnected power system; load frequency control; model-free adaptive control; radial basis function (RBF) neural network;
D O I：
10.19595/j.cnki.1000-6753.tces.211208
中图分类号：
学科分类号：
摘要：
To the problems of modeling errors and uncertainties in highly complex power systems, a load frequency control (LFC) strategy was proposed in this paper without using any model information of power system based on model-free adaptive control (MFAC) algorithm. First, the dynamic model of the power system was abstracted as a general nonlinear function. By introducing a time-varying pseudo partial derivative (PPD) between historical I/O data, the nonlinear power system was equivalent to a dynamic linear data model. Secondly, an RBF neural network was constructed to estimate the PPD of the system online, and the optimization theory was used to design the data-driven LFC scheme. In theory, the stability of the closed-loop power system and the convergence of the RBF neural network estimation method were strictly analyzed. Finally, it is verified on the interconnected power system that the LFC method in this paper can achieve good tracking performance without using model information. © 2022 Chinese Machine Press. All rights reserved.
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页码：5451 / 5461
页数：10
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