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A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm

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@article{IJASEIT7585,
   author = {Muhammad Azizan Md Idris and Mok Ren Hao and Mohd Ashraf Ahmad},
   title = {A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {1},
   year = {2019},
   pages = {18--23},
   keywords = {Moth-flame Optimization (MFO); data driven; wind plant optimization; power generation; alternative energy.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7585},
   doi = {10.18517/ijaseit.9.1.7585}
}

EndNote

%A Md Idris, Muhammad Azizan
%A Hao, Mok Ren
%A Ahmad, Mohd Ashraf
%D 2019
%T A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm
%B 2019
%9 Moth-flame Optimization (MFO); data driven; wind plant optimization; power generation; alternative energy.
%! A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm
%K Moth-flame Optimization (MFO); data driven; wind plant optimization; power generation; alternative energy.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7585
%R doi:10.18517/ijaseit.9.1.7585
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 1
%@ 2088-5334

IEEE

Muhammad Azizan Md Idris,Mok Ren Hao and Mohd Ashraf Ahmad,"A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, pp. 18-23, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.1.7585.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Md Idris, Muhammad Azizan
AU  - Hao, Mok Ren
AU  - Ahmad, Mohd Ashraf
PY  - 2019
TI  - A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 1
Y2  - 2019
SP  - 18
EP  - 23
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Moth-flame Optimization (MFO); data driven; wind plant optimization; power generation; alternative energy.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7585
DO  - 10.18517/ijaseit.9.1.7585

RefWorks

RT Journal Article
ID 7585
A1 Md Idris, Muhammad Azizan
A1 Hao, Mok Ren
A1 Ahmad, Mohd Ashraf
T1 A Data Driven Approach to Wind Plant Control using Moth-Flame Optimization (MFO) Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 1
YR 2019
SP 18
OP 23
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Moth-flame Optimization (MFO); data driven; wind plant optimization; power generation; alternative energy.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7585
DO  - 10.18517/ijaseit.9.1.7585