基于改进粒子群算法的超超临界燃煤机组负荷系统建模

Load System Modeling of Ultra-Supercritical Coal-Fired Power Unit Based on Improved Particle Swarm Optimization

针对超超临界机组负荷系统因耦合度高导致的建模困难问题及基本粒子群算法存在的缺陷,提出了一种用于负荷系统建模的改进粒子群算法,通过引入自适应、柯西变异以及梯度指导交叉思想,改善了粒子群算法容易产生早熟收敛、局部寻优能力较差等问题。将自适应柯西变异和梯度指导交叉粒子群算法与电厂的实际运行数据结合进行负荷系统建模和校验。仿真结果表明:应用该算法辨识得到的模型输出拟合现场实际数据的效果较好,算法的平均收敛速度和模型的平均精度较基本的粒子群算法有明显提升。

Aiming at the difficulties in modeling due to variables coupling of ultra-supercritical coal-fired power unit and defects in basic particle swarm optimization, an improved particle swarm optimization algorithm for modeling of load system is proposed. The algorithm introduces the idea of adaptive, Cauchy mutation and gradient guidance crossover, which improves the problems of particle swarm optimization algorithm being prone to premature convergence and has the poor local searching ability. By collecting the actual operation data of the power plant, using the adaptive Cauchy mutation and gradient guidance cross particle swarm optimization(GMGPSO) algorithm, the model established and validated. The simulation results show that the model output obtained by the GMGPSO algorithm has a good effect on fitting the actual data on site. The average convergence speed and the average accuracy both are improved a lot.