Enhanced salp swarm algorithm: Application to variable speed wind generators. (April 2019)
- Record Type:
- Journal Article
- Title:
- Enhanced salp swarm algorithm: Application to variable speed wind generators. (April 2019)
- Main Title:
- Enhanced salp swarm algorithm: Application to variable speed wind generators
- Authors:
- Qais, Mohammed H.
Hasanien, Hany M.
Alghuwainem, Saad - Abstract:
- Abstract: This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. Firstly, the enhanced salp swarm algorithm (ESSA) is proposed to improve the inadequate results of the SSA compared to the other algorithms, especially for the high dimensional functions. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. The statistical analysis of the obtained results revealed that the ESSA algorithm is significantly improved and the convergence curves showed the fast convergence to the best solution. Secondly, The SSA and ESSA algorithms are applied to enhance the maximum power point tracking and the fault-ride through ability of a grid-tied permanent magnet synchronous generator driven by a variable speed wind turbine (PMSG-VSWT). The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost. Highlights: This paper proposes an enhancement to the salp swarm algorithm (ESSA). The ESSA is tested with twenty-three benchmark functions. The ESSA is compared with eight publishedAbstract: This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. Firstly, the enhanced salp swarm algorithm (ESSA) is proposed to improve the inadequate results of the SSA compared to the other algorithms, especially for the high dimensional functions. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. The statistical analysis of the obtained results revealed that the ESSA algorithm is significantly improved and the convergence curves showed the fast convergence to the best solution. Secondly, The SSA and ESSA algorithms are applied to enhance the maximum power point tracking and the fault-ride through ability of a grid-tied permanent magnet synchronous generator driven by a variable speed wind turbine (PMSG-VSWT). The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost. Highlights: This paper proposes an enhancement to the salp swarm algorithm (ESSA). The ESSA is tested with twenty-three benchmark functions. The ESSA is compared with eight published algorithms. The ESSA and SSA are applied to the variable speed wind generators. … (more)
- Is Part Of:
- Engineering applications of artificial intelligence. Volume 80(2019)
- Journal:
- Engineering applications of artificial intelligence
- Issue:
- Volume 80(2019)
- Issue Display:
- Volume 80, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 80
- Issue:
- 2019
- Issue Sort Value:
- 2019-0080-2019-0000
- Page Start:
- 82
- Page End:
- 96
- Publication Date:
- 2019-04
- Subjects:
- Salp swarm algorithm -- Exploration and exploitation -- Permanent magnet synchronous generator -- Takagi–Sugeno–Kang fuzzy logic controller -- Wind energy conversion
Engineering -- Data processing -- Periodicals
Artificial intelligence -- Periodicals
Expert systems (Computer science) -- Periodicals
Ingénierie -- Informatique -- Périodiques
Intelligence artificielle -- Périodiques
Systèmes experts (Informatique) -- Périodiques
Artificial intelligence
Engineering -- Data processing
Expert systems (Computer science)
Periodicals
620.00285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09521976 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engappai.2019.01.011 ↗
- Languages:
- English
- ISSNs:
- 0952-1976
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3755.704500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9683.xml