Neural Network Based Data-Driven Load Frequency Control for Interconnected Power Systems
Chen Zongyao1, Bu Xuhui1,2, Guo Jinli1
1. School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454003 China; 2. Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic University Jiaozuo 454003 China
Abstract：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.
陈宗遥, 卜旭辉, 郭金丽. 基于神经网络的数据驱动互联电力系统负荷频率控制[J]. 电工技术学报, 2022, 37(21): 5451-5461.
Chen Zongyao, Bu Xuhui, Guo Jinli. Neural Network Based Data-Driven Load Frequency Control for Interconnected Power Systems. Transactions of China Electrotechnical Society, 2022, 37(21): 5451-5461.
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2022, Vol. 37 
