基于改进灰狼优化算法的校园电采暖软启动应用  

Campus electric heating soft start application based on GWO⁃IV

作　　者：王正通 程凤芹 尤文[1] 李双 WANG Zhengtong;CHENG Fengqin;YOU Wen;LI Shuang(School of Electrical and Electronic Engineering,Changchun University of Technology,Changchun 130012,China;School of Electrical and Information Engineering,Jilin University of Architecture and Technology,Changchun 130114,China)

机构地区：[1]长春工业大学电气与电子工程学院,吉林长春130012 [2]吉林建筑科技学院电气信息工程学院,吉林长春130114

出　　处：《现代电子技术》2021年第3期167-171,共5页Modern Electronics Technique

基　　金：吉林省科技发展计划项目:多智能体电供暖协调控制系统研究(2018C035-4)。

摘　　要：针对目前国内校园电采暖的房间种类多、电容器容量有限等问题提出一种新的软启动方法,能够根据重点区域升温效果、非重点区域升温效果以及总耗能之间的相对重要性在耗能与升温之间寻求平衡,该方法不仅有效降低了启动电流,同时也兼备分级升温的特点,进而避免了电容器容量的增加以及电损的增加,节约了初投资和运行成本。针对基本灰狼优化(GWO)算法收敛的不合理性以及面对复杂目标函数极易陷入局部最优的问题,改进一种凸函数形收敛因子并加入反向学习(OBL)策略,在反向学习因子中加入随机变量,使得算法在迭代过程中更具有全局性和多样性,据此提出一种改进的灰狼优化(GWO-IV)算法,通过测试单峰、多峰等23个标准测试函数证明了改进的有效性,并将GWO-IV算法应用在电采暖的温升控制。实验结果表明,GWO-IV算法在寻求最优值方面具有很强的竞争力,使得控温效果最佳。In view of the fact that the current domestic campus electric heating room are of many types,while the corresponding capacitors are of limited capacities,a new method of soft start is proposed.In the method,a balance is sought between energy consumption and temperature rise according to the relative importance of warming effect in key areas,warming effect in non⁃key areas and total energy consumption.This method not only effectively reduces the starting current,but also has the characteristics of grading temperature rise,so as to avoid the increase of capacitor capacity and electric loss,and save the initial investment and operating cost.In addition,in view of the irrationality of the convergence of primary grey wolf optimization(GWO)and the problem that it is prone to fall into local optimum in terms of complex objective function,a convex function convergence factor is improved and an opposition⁃based learning(OBL)strategy is introduced.Random variables are added into the opposition⁃based learning factor,so that the algorithm is more global and diverse in iterative process.On the basis of this,an improved grey wolf optimization⁃improved version(GWO⁃IV)is proposed.The effectiveness of the improvement is proved by testing 23 standard test functions including single peak and multi⁃peak,and the GWO⁃IV is applied to the temperature rise control of electric heating.The experimental results show that the GWO⁃IV has strong competitiveness in the aspect of seeking the optimal value,which makes the effect of temperature control the best.

关 键 词：灰狼优化算法 电采暖 软启动 分级升温 收敛因子 反向学习 

分 类 号：TN99-34[电子电信—信号与信息处理] TP391.9[电子电信—信息与通信工程]