Load frequency control of a multi-area power system based on weighting fruit fly optimization algorithm

被引：0

作者：

Wang, Nian ^{[1
]}

Zhang, Jing ^{[1
]}

Li, Bowen ^{[2
]}

He, Yu ^{[1
]}

Wang, Le ^{[1
]}

机构：

[1] School of Electrical Engineering, Guizhou University, Guiyang,550025, China

[2] Guizhou Electric Power Research Institute, Guiyang,550000, China

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Electric power transmission networks - Game theory - Multiobjective optimization - Natural resources - Particle swarm optimization (PSO) - Renewable energy resources;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

With the development and application of large-scale renewable energy sources, the electric power grid is becoming ever larger and more complicated. One of the most concerning problems is how to ensure coordination between a large number of varied controllers. Differential games theory is used to solve the problem of collaborative control. However, it is difficult to solve the differential game problem with constraints using the traditional algorithm. Furthermore, simulation models established by existing research are almost linear, which is not conducive to practical engineering application. To solve the above problem, this paper proposes a co-evolutionary algorithm based on the Weighted Fruit Fly Optimization Algorithm (WFOA) to solve a multi-area frequency collaborative control model with nonlinear constraints. Simulation results show that compared with a co-evolutionary genetic algorithm and a collaborative multi-objective particle swarm optimization algorithm, the method exhibits better control efficiency and better robustness to the changes in external disturbance and the internal unit parameters of systems. © 2020, Power System Protection and Control Press. All right reserved.

引用

页码：102 / 109