Research on Data-Driven AGC Instruction Execution Effect Recognition Method

With the high penetration of random energy such as wind power and photovoltaic in the power grid, the influence of the accuracy of regulation of traditional thermal power units on the operation of the power grid is gradually increasing. Aiming at the problem of the deviation between the actual output of thermal power units and the AGC command of the power grid, this paper proposes a data-driven AGC command execution effect identification method. Firstly, based on Kernel Principal Component Analysis (KPCA), a data preprocessing method is proposed, which maps feature datasets into low-dimensional vectors to achieve dimensionality reduction. Secondly, the Independent Recurrent Neural Network (IndRNN) is used to process and predict the dimensionality reduction data, so as to realize the accurate perception of the adjustment effect of the unit execution command. Finally, the real power grid data is used to simulate and verify the proposed method. The results show that the model can effectively reduce the deviation of instruction execution.