A Method for Optimal Tuning of Load Controller Parameters for Unit Units

Abstract

The invention discloses a parameter optimization setting method of a unit unit load controller. Firstly, two PI controllers and the unit load controlled object are used to form a machine-follower multivariable load control system; then, the deviation of unit power and main steam pressure and the corresponding deviation change rate are used to construct the optimal performance index; finally, according to the controlled The transfer function model of the object is calculated to determine the optimal search range of the two PI controller parameters, and the genetic algorithm is used to optimize the parameters of the two PI controllers. Adopting the present invention can conveniently and effectively adjust the response speed of the unit power and the dynamic deviation of the main steam pressure through the weight coefficient of the performance index, which is beneficial to the practical application of the project; the method for determining the optimal search range of the controller parameters proposed by the present invention, It can effectively improve the optimization search efficiency of the genetic algorithm.

Description

technical field [0001] The invention belongs to the technical field of automatic control, and in particular relates to a parameter optimization setting method of a unit unit load controller. Background technique [0002] The basic task of drum boiler unit load control is to make the unit load track the set value as soon as possible under the premise of ensuring the safety of unit operation (that is, the main steam pressure deviation does not exceed the limit). At present, the load control of drum boiler units generally adopts the furnace-to-machine or machine-to-boiler multivariable control system composed of PI (proportional integral) controllers. The publicly reported controller parameter optimization and setting methods generally use the following optimized performance indicators: Where α is the weight coefficient. By changing the value of the weight coefficient α, the control performance of the optimization system can be adjusted and optimized, but the ability of this ...

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Problems solved by technology

By changing the value of the weight coefficient α, the control performance of the optimization system can be adjusted and optimized, but the ability of this adjustment is limited, and it cannot cope with the complex situations and different requirements in load control, which often leads to unsatisfactory optimization and setting effects, which is not conducive to engineering. practical application

[0003] In addition, genetic algorithm is generally used for parameter optimization of unit load controllers, but how to determine the optimization range of genetic algorithm is not well resolved. At present, it is mainly determined by human experience. Too large, resulting in low optimization efficiency of the algorithm

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