Research on Intelligent Predictive AGC of a Thermal Power Unit Based on Control Performance Standards

Daogang Peng, Yue Xu and Huirong Zhao
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Daogang Peng: School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yue Xu: School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Huirong Zhao: School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2019, vol. 12, issue 21, 1-23

Abstract: In order to satisfy the growing demands of control performance and operation efficiency in the automatic generation control (AGC) system of a grid, a novel, intelligent predictive controller, combined with predictive control and neural network ideas, is proposed and applied to the AGC systems of thermal power units. This paper proposes a Bayesian neural network identification model for typical ultra-supercritical thermal power units, which was found to be accurate and can be used as a simulation model. Based on the model, this paper develops an intelligent predictive control for the AGC of thermal power units, which improves unit load operation and constitutes a novel, closed-loop AGC structure based on online control performance standard (CPS) evaluations. Intelligent predictive control is mainly improved because the neural network rolling optimization model replaces the traditional rolling optimization model in the rolling optimization module. The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under CPS can, on the one hand, improve the load tracking performance of AGC thermal power units, and, on the other hand, the controller has strong robustness. Whether the system parameters change considerably or the AGC has different grid disturbances, the new type of the loop AGC system can still sufficiently meet the control requirements of the power grid.

Keywords: automatic generation control; Bayesian neural network identification; control performance standard; intelligent predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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