Optimal micro-siting of wind farms by particle swarm optimization

Authors:
Chunqiu Wan;Jun Wang;Geng Yang;Xing Zhang
Affiliations:
Department of Automation, Tsinghua University, Beijing, China;,Department of Automation, Tsinghua University, Beijing, China;Department of Automation, Tsinghua University, Beijing, China;School of Aerospace, Tsinghua University, Beijing, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 1
Cited 3

A Study of Global Optimization Using Particle Swarms

Journal of Global Optimization

On learning to generate wind farm layouts

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Fast and effective multi-objective optimisation of wind turbine placement

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel approach to optimal placement of wind turbines in the continuous space of a wind farm The control objective is to maximize the power produced by a farm with a fixed number of turbines while guaranteeing the distance between turbines no less than the allowed minimal distance for turbine operation safety The problem of wind farm micro-siting with space constraints is formulated to a constrained optimization problem and solved by a particle swarm optimization (PSO) algorithm based on penalty functions Simulation results demonstrate that the PSO approach is more suitable and effective for micro-siting than the classical binary-coded genetic algorithms.