作者：张永涛, 曹喜果

作者单位：新疆工程学院能源工程学院，新疆 乌鲁木齐 830023

关键词：改进遗传模拟退火;超临界机组;协调控制;模型辨识

摘要：

传统的超临界机组协调控制系统模型结构复杂，不利于后续控制算法的设计，而传统的模拟退火算法在寻优规模较大时，寻优精度往往很低，遗传算法在寻优后期，寻优效率较低，也容易出现局部最优的问题。介于此，将遗传算法及模拟退火算法进行结合（SAGA），并在选择、交叉、变异算子、温度衰减函数、终止条件等方面进行改进。通过TSP问题对比测试，验证该算法较强的全局搜索能力及收敛速度。最后基于该改进算法，结合现场数据，辨识获得600 MW机组87%、66%、54% 3个负荷点下的三输入三输出模型，并通过与现场数据对比验证模型的准确性。此外，该模型形式规范，应用性强，可为协调控制系统控制设计提供参考。

Application of improved genetic simulated annealing algorithm in the identification of plant unit coordination control system
ZHANG Yongtao, CAO Xiguo
College of Energy Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China
Abstract: The traditional structure of supercritical unit coordinated control system is complex, which is not conducive to the design of subsequent control algorithm. However, the precision of traditional simulated annealing algorithm is very low, when the search scale is large, genetic algorithm is prone to the problem of local optimization in the later stage of optimization. By this in selection, crossover, mutation operator, temperature attenuation function, termination condition aspect, the paper improves the method that will combine the genetic algorithm with simulated annealing algorithm. Through the contrast test of TSP problem, the strong global search ability and convergence speed of the algorithm are verified. Finally, based on operational data, the three input three output model of 600 MW unit at 87%, 66% and 54% load points is identified by the improved algorithm, and the accuracy of the model is verified by comparing with the field data. In addition, the model is formal and applicable, which can provide reference for control design of coordinated system.
Keywords: improved genetic simulated annealing;supercritical unit;coordination control;model identification
2020, 46(8):131-136  收稿日期: 2020-04-19;收到修改稿日期: 2020-05-06
基金项目: 新疆自治区高校科研计划自然科学青年研究项目（XJEDU2018Y054）
作者简介: 张永涛（1987-），男，河南漯河市人，讲师，硕士，研究方向为热工过程控制与优化、电站机组建模及仿真等
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