第15卷第1期 智能系统学报 Vol.15 No.1 2020年1月 CAAI Transactions on Intelligent Systems Jan.2020 D0:10.11992tis.201906026 基于大变异遗传算法进行参数优化 整定的负荷频率自抗扰控制 陈增强2，黄朝阳'，孙明玮，孙青林 (1.南开大学人工智能学院，天津300350,2.天津市智能机器人重点实验室，天津300350) 摘要：本文将自抗扰控制(active disturbance rejection control,,ADRC)应用到两区域互联电力系统的负荷频率控 制(load frequency control,.LFC)中，从具有非再热式汽轮机机组的电力系统模型推广到具有水轮机机组的以及 考虑发电速率约束和调速器死区的再热式汽轮机组的电力系统模型，涉及线性、非线性和非最小相位特性3种 控制对象，并使用大变异遗传算法对控制器的参数进行整定，与基于大变异遗传算法的PI控制进行仿真比较 研究，仿真表明本文所提基于大变异遗传算法的负荷频率自抗扰控制动态响应快、偏差小、鲁棒性好、抗干扰 能力强，对于LFC系统更为有效。 关键词：自抗扰控制：负荷频率控制；大变异遗传算法；两区域互联电力系统：水轮机：发电速率约束：调速器死 区；非线性：非最小相位特性 中图分类号：TP272文献标志码：A文章编号：1673-4785(202001-0041-09 中文引用格式：陈增强，黄朝阳，孙明玮，等.基于大变异遗传算法进行参数优化整定的负荷频率自抗扰控制智能系统学 报，2020,15(1)：41-49. 英文引用格式：CHEN Zengqiang,HUANG Zhaoyang,.SUN Mingwei,,ctal.Active disturbance rejection control of load frequency based on big probability variation's genetic algorithm for parameter optimizationJ.CAAl transactions on intelligent systems, 2020,15(1):41-49. Active disturbance rejection control of load frequency based on big probability variation's genetic algorithm for parameter optimization CHEN Zengqiang2,HUANG Zhaoyang',SUN Mingwei',SUN Qinglin' (1.College of Artificial Intelligence,Nankai University,Tianjin 300350,China;2.Key Laboratory of Intelligent Robotics of Tianjin, Tianjin 300350,China) Abstract:In this paper,the active disturbance rejection control(ADRC)is applied to the load frequency control(LFC)of the two-zone interconnected power system,which is extended from a power system model with non-reheating steam tur- bines to other models,one with turbines,and another consists of reheating turbines with consideration of power generation rate constraints and governor dead zones,involving three control objects of linear,nonlinear and non-minimum phase char- acteristics.The model is used to adjust the parameters of the controller utilizing the big probability variation's genetic al- gorithm.The simulation is compared with the PI control based on the big probability variation's genetic algorithm.The simulation shows that ADRC based on big probability variation's genetic algorithm possesses fast dynamic response,small deviation,good robustness,strong anti-interference characteristics,which is more effective for the LFC system. Keywords:active disturbance rejection control;load frequency control;big probability variation's genetic algorithm; two-area interconnected power system;turbine;generation rate constraint;governor's dead zone;nonlinear,non-minimum phase characteristics 收稿日期：2019-06-14 电力系统频率是电能质量的一个基本指标。 基金项目：国家自然科学基金项目(61973175,61573197， 电力系统的负荷是时刻变化的，任何一处负荷的 61973172). 通信作者：陈增强.E-mail:chenzq@nankai..edu.cn 变化，都会引起全系统功率的不平衡，导致频率DOI: 10.11992/tis.201906026 基于大变异遗传算法进行参数优化 整定的负荷频率自抗扰控制 陈增强1,2，黄朝阳1 ，孙明玮1 ，孙青林1 （1. 南开大学 人工智能学院，天津 300350; 2. 天津市智能机器人重点实验室，天津 300350） 摘 要：本文将自抗扰控制 (active disturbance rejection control，ADRC) 应用到两区域互联电力系统的负荷频率控 制 (load frequency control，LFC) 中，从具有非再热式汽轮机机组的电力系统模型推广到具有水轮机机组的以及 考虑发电速率约束和调速器死区的再热式汽轮机组的电力系统模型，涉及线性、非线性和非最小相位特性 3 种 控制对象，并使用大变异遗传算法对控制器的参数进行整定，与基于大变异遗传算法的 PI 控制进行仿真比较 研究，仿真表明本文所提基于大变异遗传算法的负荷频率自抗扰控制动态响应快、偏差小、鲁棒性好、抗干扰 能力强，对于 LFC 系统更为有效。 关键词：自抗扰控制；负荷频率控制；大变异遗传算法；两区域互联电力系统；水轮机；发电速率约束；调速器死 区；非线性；非最小相位特性 中图分类号：TP272 文献标志码：A 文章编号：1673−4785(2020)01−0041−09 中文引用格式：陈增强, 黄朝阳, 孙明玮, 等. 基于大变异遗传算法进行参数优化整定的负荷频率自抗扰控制 [J]. 智能系统学 报, 2020, 15(1): 41–49. 英文引用格式：CHEN Zengqiang, HUANG Zhaoyang, SUN Mingwei, et al. Active disturbance rejection control of load frequency based on big probability variation’s genetic algorithm for parameter optimization[J]. CAAI transactions on intelligent systems, 2020, 15(1): 41–49. Active disturbance rejection control of load frequency based on big probability variation’s genetic algorithm for parameter optimization CHEN Zengqiang1,2 ，HUANG Zhaoyang1 ，SUN Mingwei1 ，SUN Qinglin1 (1. College of Artificial Intelligence, Nankai University, Tianjin 300350, China; 2. Key Laboratory of Intelligent Robotics of Tianjin, Tianjin 300350, China) Abstract: In this paper, the active disturbance rejection control (ADRC) is applied to the load frequency control (LFC) of the two-zone interconnected power system, which is extended from a power system model with non-reheating steam tur￾bines to other models, one with turbines, and another consists of reheating turbines with consideration of power generation rate constraints and governor dead zones, involving three control objects of linear, nonlinear and non-minimum phase char￾acteristics. The model is used to adjust the parameters of the controller utilizing the big probability variation’s genetic al￾gorithm. The simulation is compared with the PI control based on the big probability variation’s genetic algorithm. The simulation shows that ADRC based on big probability variation’s genetic algorithm possesses fast dynamic response, small deviation, good robustness, strong anti-interference characteristics, which is more effective for the LFC system. Keywords: active disturbance rejection control; load frequency control; big probability variation’s genetic algorithm; two-area interconnected power system; turbine; generation rate constraint; governor’s dead zone; nonlinear; non-minimum phase characteristics 电力系统频率是电能质量的一个基本指标[1]。 电力系统的负荷是时刻变化的，任何一处负荷的 变化，都会引起全系统功率的不平衡，导致频率 收稿日期：2019−06−14. 基金项目：国家自然科学基金项目（ 61973175， 61573197， 61973172）. 通信作者：陈增强. E-mail：chenzq@nankai.edu.cn. 第 15 卷第 1 期 智 能 系 统 学 报 Vol.15 No.1 2020 年 1 月 CAAI Transactions on Intelligent Systems Jan. 2020