基于改进型人群搜索算法的火电机组深调工况主汽压力控制方法  

Improved SOA Algorithm-Based Main Steam Pressure Control Method of Thermal Power Unit under Deep Peak Regulation Condition

作　　者：张彪 徐俊[2] ZHANG Biao;XU Jun(Electric Power Research Institute,State Grid Hubei Electric Power Co.,Ltd.,Wuhan Hubei 430077,China;China State Key Laboratory of Coal Combustion,Huazhong University of Science and Technology,Wuhan Hubei 430074,China)

机构地区：[1]国网湖北省电力有限公司电力科学研究院,湖北武汉430077 [2]华中科技大学煤燃烧国家重点实验室,湖北武汉430074

出　　处：《湖北电力》2023年第3期103-108,共6页Hubei Electric Power

基　　金：湖北省重点研发计划(项目编号:2022BCA063)。

摘　　要：火电机组主汽压力本身具有的大滞后、大惯性和非线性等控制特性在深度调峰工况下表现得尤为明显,严重制约了火电机组调峰爬坡速率,也不利于机组安全稳定运行。针对这一问题,提出一种改进型人群搜索算法(Seeker Optimization Algorithm,SOA)优化PID控制火电机组深度调峰工况下主汽压力的方法,采用反向差分进化机制和自适应t分布策略对原始人群搜索算法进行联合改进,进一步增强其脱离局部最优的能力。仿真结果及工程应用表明,改进型SOA算法具有更好的寻优能力及优化效率,主汽压力跟踪设定值的性能良好,且具有很好的鲁棒性。The main steam pressure of thermal power unit itself has the control characteristics of large lag,large inertia,nonlinear,etc.,which is particularly obvious in deep peak regulation condition,seriously restricting the peak load climbing rate of thermal power unit,and also unfavorable for the safe and stable operation of the unit.In order to solve the above problem,this paper proposes a method of using an improved seeker optimization algorithm(SOA)to optimize the main steam pressure in the condition of PID controlling thermal power unit’s deep peak regulation,and then uses the reverse differential evolution mechanism and adaptive T-distribution strategy to jointly improve the original SOA to further enhance its ability to deviate from local optima.Simulation results and engineering applications show that the improved SOA algorithm has better optimization ability and optimization efficiency,and also has good performance and robustness of main steam pressure tracking setpoint.

关 键 词：碳达峰 碳中和 深度调峰 主汽压力 人群搜索算法 

分 类 号：TK323[动力工程及工程热物理—热能工程]