InECCE2019 pp 645-654 | Cite as

A Salp Swarm Algorithm to Improve Power Production of Wind Plant

  • Ahmad Zairi Mohd-Zain
  • Mohd Ashraf Ahmad
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)


Currently, the main problem of wind plant power production is definitely the control system of a wind generator that is not able to cope with the impact of turbulence and thus weakens complete power output. In this paper a Salp Swarm Algorithm (SSA) is proposed as a data-driven method to improve the controller variable and thus optimize the complete power production of the wind plant. The SSA is among of the meta-heuristic technique and imitates the salps chain’s swarm movement depending on the food placement. The model used in this study originates from Denmark’s actual Horns Rev wind plant. The analysis result demonstrates the SSA generates significantly better total wind power production as opposed to the Spiral Dynamic Algorithm (SDA) and the Particle Swarm Optimization (PSO) technique.


Salp swarm algorithm (SSA) Data-driven Wind plant optimization Power production 



Our study for this project was assisted by UMP and Ministry of Education (MOE) under Fundamental Research Grant Scheme with reference no. FRGS/1/2017/ICT02/UMP/02/2 or RDU170104.


  1. 1.
    Park J, Law KH (2016) A data-driven, cooperative wind plant control to maximize the total power production. Appl Energy 165:151–165CrossRefGoogle Scholar
  2. 2.
    Marden JR, Ruben SD, Pao LY (2012) Surveying game theoretic approaches for wind plant optimization. In: Proceedings of the AIAA aerospace sciences meeting, Nashville, Tennessee, pp 1–10Google Scholar
  3. 3.
    Marden JR, Ruben SD, Pao LY (2013) A model-free approach to wind plant control using game theoretic methods. IEEE Trans Control Syst Technol 21(4):1207–1214CrossRefGoogle Scholar
  4. 4.
    Gebraad PM, van Dam FC, van Wingerden JW (2013) A model-free distributed approach for wind plant control. In: Proceedings american control conference, Washington DC, USA, pp 628–633Google Scholar
  5. 5.
    Gebraad PM, van Wingerden JW (2015) Optimum power-point tracking control for wind plants. Wind Energy 18(3):429–447CrossRefGoogle Scholar
  6. 6.
    Ahmad MA, Azuma SI, Sugie T (2014) A model-free approach for maximizing power production of wind plant using multi-resolution simultaneous perturbation stochastic approximation. Energies 7(9):5624–5646CrossRefGoogle Scholar
  7. 7.
    Ahmad MA, Hao MR, Raja Ismail RMT, Nasir ANK (2016) Model-free wind plant control based on random search. In: Proceedings of IEEE international conference on automatic control and intelligent systems, Shah Alam, Malaysia, pp 131–134Google Scholar
  8. 8.
    Hao MR, Raja Ismail RMT, Ahmad MA (2017) Using spiral dynamic algorithm for maximizing power production of wind plant. In: Proceedings of IEEE International conference on applied system innovation, Sapporo, Japan, pp 1706–1709Google Scholar
  9. 9.
    Md Idris MA, Hao MR, Ahmad MA (2019) A data driven approach to wind plant control using moth-flame optimization (MFO) algorithm. Int J Adv Sci Eng Inf Technol 18–23Google Scholar
  10. 10.
    Suid MH, Mohd Tumari MZ, Ahmad MA (2019) A modified sine cosine algorithm for improving wind plant energy production. Int J Electr Eng Comput Sci 16(1):101–106Google Scholar
  11. 11.
    Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp Swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191CrossRefGoogle Scholar
  12. 12.
    Hadad Baygi SM, Karsaz A, Elahi A (2018) A hybrid optimal PID-Fuzzy control design for seismic exited structural system against earthquake: a salp swarm algorithm. In: 6th Iranian joint congress on fuzzy and intelligent system, Kerman, Iran, pp 220–225Google Scholar
  13. 13.
    Tolba M, Rezk H, Zaki Diab AA et al (2018) A novel robust methodology based Salp Swarm algorithm for allocation and capacity of renewable distributed generators on distribution grids 11(10):1–34Google Scholar
  14. 14.
    El-Fergany A (2018) Extracting optimal variables of PEM fuel cells using Salp Swarm optimizer. Renew Energy 119:641–648CrossRefGoogle Scholar
  15. 15.
    Wang J, Gao Y, Chen X (2018) A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective Salp Swarm algorithm for short-term load forecasting. Energies 11:1–30CrossRefGoogle Scholar
  16. 16.
    Ibrahim A, Ahmed A, Hussein S et. al (2018) Fish image segmentation using salp swarm algorithm. In: The international conference on advanced machine learning technologies and applications, pp 42–51Google Scholar
  17. 17.
    Yodphet D, Onlam A, Siritaratiwat A et al (2019) Electrical distribution system reconfiguration for power loss reduction by the Salp Swarm algorithm. Int J Smart Grid Clean Energy 8:156–163CrossRefGoogle Scholar
  18. 18.
    Ekinci S, Hekimoglu B (2018) Variable optimization of power system stabilizer via Salp Swarm Algorithm. In: 5th international conference on electrical and electronic engineering, Istanbul, Turki, pp 143–147Google Scholar
  19. 19.
    Hussein AG, Hassanien AE, Houssein EH (2017) Swarming behaviour of salp algorithm for predicting chemical compound activities. In: Eighth international conference on intelligent computing and information systems, Cairo, Egypt, pp 315–320Google Scholar
  20. 20.
    Zhang J, Wang Z, Luo X (2018) Variable estimation of the soil water retention curve using the Salp Swarm algorithm. Water 10:815CrossRefGoogle Scholar
  21. 21.
    Scholbrock AK (2011) Optimizing wind plant control strategies to minimize wake loss effects. Univ. Colo, BoulderGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ahmad Zairi Mohd-Zain
    • 1
  • Mohd Ashraf Ahmad
    • 1
  1. 1.Faculty of Electrical & Electronics EngineeringUniversiti Malaysia PahangKuantanMalaysia

Personalised recommendations