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.
Highlights
With the increasing complexity and scale of modern power system structures, more and more uncertain disturbances have brought tremendous new challenges to power grid security
The simulation results indicate that the intelligent predictive controller developed in the two-area interconnected power grid under control performance standard (CPS) can, on the one hand, improve the load tracking performance of automatic generation control (AGC) thermal power units, and, on the other hand, the controller has strong robustness
Identification of of an an AGC Intelligent Control System Based on CPS
Summary
With the increasing complexity and scale of modern power system structures, more and more uncertain disturbances have brought tremendous new challenges to power grid security. Nanda et al [12] proposed using the genetic algorithm (GA) to optimize control of the AGC system This method has some shortcomings, as GA only carries out crossover and mutation operations, and it is likely to fall into local optimum or premature convergence. Intelligent predictive AGC of thermal power units based on control performance standards are used to improve the energy-saving ability and operation efficiency of AGC and enhance the adaptability of interconnected power grids to CPS. First, aIt Bayesian from a powerand plant, with intelligent control and classical predictive control avoids theneural error network the model for typicalmodel ultra-supercritical thermal unitsenables was developed. Units the experiment proved that improved the intelligent controller in the for thermal power was developed, which unitpredictive load operations anddeveloped formed a novel, two-area interconnected power under control. System parameters change considerably, or different grid disturbances are added, the new type of the loop AGC system can still meet the control requirements of the power grid very well
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