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A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm

One of the main issues of the wind plant power generation nowadays is that the current stand alone controller of each turbine in the wind plant is not able to cope with chaotic nature of wake aerodynamic effect. Therefore, it is necessary to re-tune the controller of each turbine in the wind plant s...

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Main Authors: Idris, M. A. M, Hao, Mok Ren, Mohd Ashraf, Ahmad
Format: Article
Language:English
Published: Insight Society 2019
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/24888/1/A%20Data%20Driven%20Approach%20to%20Wind%20Plant%20Control.pdf
http://umpir.ump.edu.my/id/eprint/24888/
https://doi.org/10.18517/ijaseit.9.1.7585
https://doi.org/10.18517/ijaseit.9.1.7585
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spelling my.ump.umpir.248882019-10-11T07:21:05Z http://umpir.ump.edu.my/id/eprint/24888/ A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm Idris, M. A. M Hao, Mok Ren Mohd Ashraf, Ahmad TK Electrical engineering. Electronics Nuclear engineering One of the main issues of the wind plant power generation nowadays is that the current stand alone controller of each turbine in the wind plant is not able to cope with chaotic nature of wake aerodynamic effect. Therefore, it is necessary to re-tune the controller of each turbine in the wind plant such that the total power generation is improved. This article presents an investigation of a data driven approach using moth-flame optimization algorithm (MFO) to the problem of improving wind plants power generation. The MFO based technique is applied to search the turbine’s optimum controller such that the aggregation power generation of a wind plant is maximized. The MFO is a population based optimization method that mimics the behavior of moths that navigate on specific angle with respect to the moon location. Here, it is expected that the MFO can solve the control accuracy problem in the existing algorithms for maximizing wind plant. A row of wind turbines plant with wake aerodynamic effect among turbines is adopted to demonstrate the effectiveness of the MFO based technique. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The performance of the proposed MFO algorithm is analyzed in terms of the statistical analysis of the total power generation. Numerical results show that the MFO based approach generates better total wind power generation than spiral dynamic algorithm (SDA) based approach and safe experimentation dynamics (SED) based approach. Insight Society 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24888/1/A%20Data%20Driven%20Approach%20to%20Wind%20Plant%20Control.pdf Idris, M. A. M and Hao, Mok Ren and Mohd Ashraf, Ahmad (2019) A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm. International Journal on Advanced Science, Engineering and Information Technology, 9 (1). pp. 18-23. ISSN 2088-5334 https://doi.org/10.18517/ijaseit.9.1.7585 https://doi.org/10.18517/ijaseit.9.1.7585
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Idris, M. A. M
Hao, Mok Ren
Mohd Ashraf, Ahmad
A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm
description One of the main issues of the wind plant power generation nowadays is that the current stand alone controller of each turbine in the wind plant is not able to cope with chaotic nature of wake aerodynamic effect. Therefore, it is necessary to re-tune the controller of each turbine in the wind plant such that the total power generation is improved. This article presents an investigation of a data driven approach using moth-flame optimization algorithm (MFO) to the problem of improving wind plants power generation. The MFO based technique is applied to search the turbine’s optimum controller such that the aggregation power generation of a wind plant is maximized. The MFO is a population based optimization method that mimics the behavior of moths that navigate on specific angle with respect to the moon location. Here, it is expected that the MFO can solve the control accuracy problem in the existing algorithms for maximizing wind plant. A row of wind turbines plant with wake aerodynamic effect among turbines is adopted to demonstrate the effectiveness of the MFO based technique. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The performance of the proposed MFO algorithm is analyzed in terms of the statistical analysis of the total power generation. Numerical results show that the MFO based approach generates better total wind power generation than spiral dynamic algorithm (SDA) based approach and safe experimentation dynamics (SED) based approach.
format Article
author Idris, M. A. M
Hao, Mok Ren
Mohd Ashraf, Ahmad
author_facet Idris, M. A. M
Hao, Mok Ren
Mohd Ashraf, Ahmad
author_sort Idris, M. A. M
title A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm
title_short A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm
title_full A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm
title_fullStr A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm
title_full_unstemmed A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm
title_sort data driven approach to wind plant control using moth-flame optimization (mfo) algorithm
publisher Insight Society
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/24888/1/A%20Data%20Driven%20Approach%20to%20Wind%20Plant%20Control.pdf
http://umpir.ump.edu.my/id/eprint/24888/
https://doi.org/10.18517/ijaseit.9.1.7585
https://doi.org/10.18517/ijaseit.9.1.7585
_version_ 1648741171733725184
score 13.211869

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