Differential Evolution with a New Encoding Mechanism for Optimizing Wind Farm Layout

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

63 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1040-1054
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume14
Issue number3
Online published22 Sep 2017
Publication statusPublished - Mar 2018

Abstract

This paper presents a differential evolution algorithm with a new encoding mechanism for efficiently solving the optimal layout of the wind farm, with the aim of maximizing the power output. In the modeling of the wind farm, the wake effects among different wind turbines are considered and the Weibull distribution is employed to estimate the wind speed distribution. In the process of evolution, a new encoding mechanism for the locations of wind turbines is designed based on the characteristics of the wind farm layout. This encoding mechanism is the first attempt to treat the location of each wind turbine as an individual. As a result, the whole population represents a layout. Compared with the traditional encoding, the advantages of this encoding mechanism are twofold: 1) the dimension of the search space is reduced to two, and 2) a crucial parameter (i.e., the population size) is eliminated. In addition, differential evolution serves as the search engine and the caching technique is adopted to enhance the computational efficiency. The comparative analysis between the proposed method and seven other state-of-the-art methods is conducted based on two wind scenarios. The experimental results indicate that the proposed method is able to obtain the best overall performance, in terms of the power output and execution time.

Research Area(s)

  • Differential evolution (DE), encoding mechanism, optimization, wake effect, wind farm layout