Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm

Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm

This paper presents a preliminary study of a model-free approach based on spiral dynamic algorithm (SDA) for maximizing wind farms power production. The SDA based approach is utilized to find the optimal control parameter of each turbine to maximize the total power production of a wind farm. For simp...

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Main Authors: Mok, Ren Hao, Raja Mohd Taufika, Raja Ismail, Mohd Ashraf, Ahmad
Format: Conference or Workshop Item
Language:English
Published: IEEE 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/17633/
http://umpir.ump.edu.my/id/eprint/17633/1/ICASI2017.pdf
id ump-17633
recordtype eprints
spelling ump-176332018-02-02T08:04:16Z http://umpir.ump.edu.my/id/eprint/17633/ Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm Mok, Ren Hao Raja Mohd Taufika, Raja Ismail Mohd Ashraf, Ahmad TK Electrical engineering. Electronics Nuclear engineering This paper presents a preliminary study of a model-free approach based on spiral dynamic algorithm (SDA) for maximizing wind farms power production. The SDA based approach is utilized to find the optimal control parameter of each turbine to maximize the total power production of a wind farm. For simplicity, a single row wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the SDA based method produces higher total power production compared to the particle swarm optimization (PSO) and game theoretic (GT) based approaches. IEEE 2017-05-16 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17633/1/ICASI2017.pdf Mok, Ren Hao and Raja Mohd Taufika, Raja Ismail and Mohd Ashraf, Ahmad (2017) Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm. In: IEEE International Conference on Applied System Innovation (ICASI 2017), 13-17 May 2017 , Sapporo, Japan. pp. 1-4..
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mok, Ren Hao
Raja Mohd Taufika, Raja Ismail
Mohd Ashraf, Ahmad
Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm
description This paper presents a preliminary study of a model-free approach based on spiral dynamic algorithm (SDA) for maximizing wind farms power production. The SDA based approach is utilized to find the optimal control parameter of each turbine to maximize the total power production of a wind farm. For simplicity, a single row wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the SDA based method produces higher total power production compared to the particle swarm optimization (PSO) and game theoretic (GT) based approaches.
format Conference or Workshop Item
author Mok, Ren Hao
Raja Mohd Taufika, Raja Ismail
Mohd Ashraf, Ahmad
author_facet Mok, Ren Hao
Raja Mohd Taufika, Raja Ismail
Mohd Ashraf, Ahmad
author_sort Mok, Ren Hao
title Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm
title_short Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm
title_full Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm
title_fullStr Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm
title_full_unstemmed Using Spiral Dynamic Algorithm for Maximizing Power Production of Wind Farm
title_sort using spiral dynamic algorithm for maximizing power production of wind farm
publisher IEEE
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/17633/
http://umpir.ump.edu.my/id/eprint/17633/1/ICASI2017.pdf
first_indexed 2023-09-18T22:24:28Z
last_indexed 2023-09-18T22:24:28Z
_version_ 1777415874183954432

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