Optimal coordinated voltage control of power systems
被引:1
作者:
李艳君
论文数: 0引用数: 0
h-index: 0
机构:
Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, ChinaInstitute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China
李艳君
[1
]
HILL David J
论文数: 0引用数: 0
h-index: 0
机构:
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, ChinaInstitute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China
HILL David J
[2
]
吴铁军
论文数: 0引用数: 0
h-index: 0
机构:
Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, ChinaInstitute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China
吴铁军
[1
]
机构:
[1] Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China
[2] Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
Power systems;
Voltage control;
Immune algorithm;
Global optimization;
D O I:
暂无
中图分类号:
TM76 [电力系统的自动化];
学科分类号:
080802 ;
摘要:
An immune algorithm solution is proposed in this paper to deal with the problem of optimal coordination of local physically based controllers in order to preserve or retain mid and long term voltage stability. This problem is in fact a global coordination control problem which involves not only sequencing and timing different control devices but also tuning the pa- rameters of controllers. A multi-stage coordinated control scheme is presented, aiming at retaining good voltage levels with minimal control efforts and costs after severe disturbances in power systems. A self-pattern-recognized vaccination procedure is developed to transfer effective heuristic information into the new generation of solution candidates to speed up the convergence of the search procedure to global optima. An example of four bus power system case study is investigated to show the effectiveness and efficiency of the proposed algorithm, compared with several existing approaches such as differential dynamic programming and tree-search.