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author:

Jiang, Y.-W. (Jiang, Y.-W..) [1] | Chen, C. (Chen, C..) [2] | Wen, B.-Y. (Wen, B.-Y..) [3]

Indexed by:

Scopus PKU CSCD

Abstract:

It is helpful to install reactive compensation equipment at wind turbine terminals to improve the bad influence of wind power on the voltage quality of power system. Because of the random feature of wind power and change variation of the load and system's voltage, a mathematical model based on chance constrained programming (CCP) is given. And an algorithm of stochastic simulation's particle swarm optimization is put forward to figure out the reactive power compensation capacity. The model's objective function is to minimize cost under consideration of probability distribution of wind speed, the characteristic of active and reactive power of wind turbines, the way of power flow calculation in wind power integrated system, and so on. By analyzing the variation of reactive power, voltage and power factor of wind farm and utilizing the given way to optimize reactive power compensation in a distribution network, the model and algorithm are proved effective.

Keyword:

Chance constrained programming; Particle swarm optimization; Power flow calculation; Reactive power compensation; Stochastic simulation; Wind farm; Wind turbine

Community:

  • [ 1 ] [Jiang, Y.-W.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350002, China
  • [ 2 ] [Chen, C.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350002, China
  • [ 3 ] [Wen, B.-Y.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350002, China

Reprint 's Address:

  • [Jiang, Y.-W.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350002, China

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Source :

Proceedings of the Chinese Society of Electrical Engineering

ISSN: 0258-8013

Year: 2008

Issue: 13

Volume: 28

Page: 47-52

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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