基于优化PSO算法的SCR 脱硝控制器研究
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摘要
针对SCR脱硝控制系统中的PID参数整定困难,提出一种优化粒子群算法的PID控制器参数整定方法。该算法利用已知粒子信息得到适应度函数值的估值,在估值策略中,引入粒子间的相似度和粒子的可信度评价方法,增加适应度函数值估计次数和准确性,减少适应度函数值计算次数,提高算法性能。将优化的粒子群算法用于优化SCR脱硝系统中PID控制器参数,与传统整定PID控制器参数方法相比,基于适应值估值策略的粒子群算法整定PID控制器参数收敛速度快,系统内回路响应上升速度快、时间短,外回路静态误差小、无超调等优点,较好地满足SCR脱硝系统的动态特性。
Aiming at the difficulty of PID parameter setting in SCR denitration control system, a PID controller parameter tuning method based on particle swarm optimization algorithm is proposed. The algorithm uses the known particle information to obtain the approximation of the fitness function value. In the evaluation strategy, the similarity between particles and the credibility evaluation method of the particle are introduced to increase the probability and accuracy of the fitness function value estimation and reduce the adaptation. The degree of function value is calculated to improve the performance of the algorithm. The optimized particle swarm optimization algorithm is used to optimize the PID controller parameters in the SCR denitration system. Compared with the traditional tuning PID controller parameter method, the particle swarm optimization algorithm based on the adaptive value estimation strategy converges the PID controller parameters quickly. The loop response rises quickly, and the time is short; the external loop static error is small, and there is no overshoot, which better satisfies the dynamic characteristics of the SCR denitration system.
Aiming at the difficulty of PID parameter setting in SCR denitration control system, a PID controller parameter tuning method based on particle swarm optimization algorithm is proposed. The algorithm uses the known particle information to obtain the approximation of the fitness function value. In the evaluation strategy, the similarity between particles and the credibility evaluation method of the particle are introduced to increase the probability and accuracy of the fitness function value estimation and reduce the adaptation. The degree of function value is calculated to improve the performance of the algorithm. The optimized particle swarm optimization algorithm is used to optimize the PID controller parameters in the SCR denitration system. Compared with the traditional tuning PID controller parameter method, the particle swarm optimization algorithm based on the adaptive value estimation strategy converges the PID controller parameters quickly. The loop response rises quickly, and the time is short; the external loop static error is small, and there is no overshoot, which better satisfies the dynamic characteristics of the SCR denitration system.
引文
[1]张庆科. 粒子群优化算法及差分进行算法研究 [D].济南:山东大学, 2017.
[2]唐玉兰,徐明亮,梅娟,等. 粒子群算法在PID控制器参数整定中的研究与应用[J].计算机工程与应用, 2012, 48(34): 221-224.
[3]曹方,王伟,王介生,等. 基于 HS-PSO 算法的PID 控制器参数自整定[J].控制工程, 2011, 18(3): 352-355.
[4]刘彤. 微粒群进化估值策略的扩展研究[D].太原:太原科技大学,2015.
[5]刘建华,樊晓平,瞿志华. 一种基于相似度的粒子群算法[J].控制与决策,2007, 22(10): 1155-1159.
[6]陶元芳,刘晓光. 一种应用ARPSO优化RBF神经网络的方法[J].计算机技术与发展,2014,24(11):43-46.
[7]胡艳. 大型火电机组优化目标函数选取方法的研究与应用[D]. 保定:华北电力大学, 2017.
[8]杨景明,侯新培,崔慧慧,等.基于融合多策略改进的多目标粒子群优化算法[J]. 控制与决策,2018,33(2):226-234.
[9]杨虎. 粒子群多目标优化算法的研究与应用[D].合肥:安徽理工大学,2013.
[10]杨智,陈颖. 改进粒子群算法及其在PID整定中的应用[J].控制工程,2016,23(2):161-166.
[11]刘延泉,杨堃,王如蓓. 基于互信息和PSO-LSSVM的脱硝反应器入口NOx浓度预测[J]. 电力科学与工程,2018,34(9):67-72.
[2]唐玉兰,徐明亮,梅娟,等. 粒子群算法在PID控制器参数整定中的研究与应用[J].计算机工程与应用, 2012, 48(34): 221-224.
[3]曹方,王伟,王介生,等. 基于 HS-PSO 算法的PID 控制器参数自整定[J].控制工程, 2011, 18(3): 352-355.
[4]刘彤. 微粒群进化估值策略的扩展研究[D].太原:太原科技大学,2015.
[5]刘建华,樊晓平,瞿志华. 一种基于相似度的粒子群算法[J].控制与决策,2007, 22(10): 1155-1159.
[6]陶元芳,刘晓光. 一种应用ARPSO优化RBF神经网络的方法[J].计算机技术与发展,2014,24(11):43-46.
[7]胡艳. 大型火电机组优化目标函数选取方法的研究与应用[D]. 保定:华北电力大学, 2017.
[8]杨景明,侯新培,崔慧慧,等.基于融合多策略改进的多目标粒子群优化算法[J]. 控制与决策,2018,33(2):226-234.
[9]杨虎. 粒子群多目标优化算法的研究与应用[D].合肥:安徽理工大学,2013.
[10]杨智,陈颖. 改进粒子群算法及其在PID整定中的应用[J].控制工程,2016,23(2):161-166.
[11]刘延泉,杨堃,王如蓓. 基于互信息和PSO-LSSVM的脱硝反应器入口NOx浓度预测[J]. 电力科学与工程,2018,34(9):67-72.