摘要
为了解决传统混合电源统一电力系统的自动发电控制问题,提出了一种级联PD-模糊PID控制器。采用灰狼优化和基于教与学优化算法的混合优化(GWO-TLBO)技术对该级联PD-模糊PID控制器的缩放参数进行优化。在优化过程中,在区域1加入0.01 pu的突发负载干扰,并以积分时间绝对误差作为评价函数。在分析系统的动态性能时,考虑了交流联络线和交流-直流联络线。在负载随机变化和标称参数变化情况下,进一步验证了级联PD-模糊PID控制器的鲁棒性和灵敏度,利用时间响应指标对系统的动态特性进行分析。通过提出方法获得的最优参数与现有成果进行比较,得出使用级联PD-模糊PID控制器的Δf1、Δf2及ΔPtie沉降时间相比PID分别提高了93.36%、84.46%、91.13%,相比模糊PID分别提高了82.59%、56.15%、65.06%。仿真结果表明,级联PD-模糊PID控制器优于其他控制器,解决了传统混合电源统一电力系统的自动发电控制问题,可为电力系统自动发电控制提供借鉴。
In order to solve the problem of automatic generation control of traditional hybrid unified power system,a cascade PD-fuzzy PID controller was proposed.The scaling parameters of the cascade PD-fuzzy PID controller were optimized by using gray wolf optimization and hybrid optimization based on teaching and learning optimization algorithm(GWO-TLBO).In the optimization process,the burst load interference of 0.01 pu was added into region 1,and the absolute error of integral time was taken as the evaluation function.In the analysis of the dynamic performance of the system,the AC link and AC-DC link are considered.The robustness and sensitivity of the cascade PD-fuzzy PID controller are further verified in the case of random load variation and nominal parameter variation.The dynamic characteristics of the system are analyzed by using the time response index.By comparing the optimal parameters obtained by the proposed method with the existing results,it is concluded that the settling time ofΔf1,Δf2 andΔPtie using the cascade PD-fuzzy PID controller is increased by 93.36%,84.46%and 91.13%respectively compared with PID,and by 82.59%,56.15%and 65.06%respectively compared with the fuzzy PID.The simulation results show that the cascade PD-fuzzy PID controller is superior to other controllers,and solves the problem of automatic generation control of traditional hybrid power system,which can provide reference for automatic generation control of power system.
作者
范璟
Fan Jing(School of Mechanical and Material Engineering,Xi'an University,Xi'an 710065,China)
出处
《电子测量技术》
北大核心
2021年第3期87-92,共6页
Electronic Measurement Technology
基金
西安文理学院课题(HL2020-65)项目资助。