[1]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.[doi:10.11992/tis.201906026]
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Active disturbance rejection control of load frequency based on big probability variation’s genetic algorithm for parameter optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 1 Page number: 41-49 Column: 学术论文—智能系统 Public date: 2020-01-05
Title:
Active disturbance rejection control of load frequency based on big probability variation’s genetic algorithm for parameter optimization
Author(s):
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
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
CLC:
TP272
DOI:
10.11992/tis.201906026
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 turbines 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 characteristics. The model is used to adjust the parameters of the controller utilizing the big probability variation’s genetic algorithm. 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.
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