针对火电厂负荷控制系统因强耦合性强非线性等特点而难以对其建立精确热工模型的问题，结合工程实际分析三输入三输出负荷控制对象的动态特性，将免疫算法(Immunity Algorithm，IA)的免疫记忆功能引入粒子群算法(Particle Swarm Optimization，PSO)，形成免疫记忆粒子群算法（Immune Memory PSO，IM-PSO）并运用在超超临界火电机组负荷控制对象的模型辨识中。辨识结果表明IM-PSO相对于普通PSO收敛速度提高了50%，收敛精度提高了6.08%，改善了PSO易早熟、粒子后期相似度过高的缺点，同时也验证了IM-PSO对于大型火电机组负荷控制对象辨识的有效性。

The accurate load control model is the basis for timely adjustment of the power generation side to ensure stable operation of the power grid side.It is difficult to establish an accurate thermal model for the thermal power plant load control system due to the characteristics of strong coupling and strong nonlinearity.In this paper,the dynamic characteristics of the threeinput and three-output load control objects are analyzed.For the disadvantages of the traditional particle swarm optimization algorithm which is easy to fall into local optimum and lack of late particle diversity,the immune memory function of the immune algorithm (IA) is introduced into the particle swarm algorithm (PSO) to form an immune memory particle swarm algorithm (IM-PSO),and is used in the model identification of the load control objects for ultrasupercritical thermal power units.The identification results show that the convergence speed of IM-PSO is increased by 50% compared with that of ordinary PSO,and the convergence accuracy is improved by 6.08%.This overcomes the shortcomings of premature maturity of PSO and high similarity of particles in later period,and it also verifies that IM-PSO is effective for the load control object identification for large thermal power units.

TM621.2

上海市"科技创新行动计划"高新技术领域项目（17511109402）