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Optimal Power Flow Solutions for Power System Operations Using Moth-Flame Optimization Algorithm

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Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 (NUSYS 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 666))

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Abstract

This article proposes a recent novel metaheuristic optimization technique: Moth-Flame Optimizer (MFO) to solve one of the most important problems in the power system namely Optimal power flow (OPF). Three objective functions will be solved simultaneously: minimizing fuel cost, transmission loss, and voltage deviation minimization using a weighted factor. To show the effectiveness of proposed MFO in solving the mentioned problem, the IEEE 30-bus test system will be used. Then the obtained result from the MFO algorithm is compared with other selected well-known algorithms. The comparison proves that MFO gives better results compared to the other compared algorithms. MFO gives a reduction of 14.50% compared to 13.38 and 14.15% for artificial bee colony (ABC) and Improved Grey Wolf Optimizer (IGWO) respectively.

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References

  1. Ayan K, Kiliç U (2012) Artificial bee colony algorithm solution for optimal reactive power flow. Appl Soft Comput J 12(5):1477–1482

    Article  Google Scholar 

  2. Shaw B, Mukherjee V, Ghoshal SP (2014) Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Int J Electr Power Energy Syst 55:29–40

    Article  Google Scholar 

  3. Sulaiman MH, Mustaffa Z, Mohamed MR, Aliman O (2015) Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl Soft Comput J 32:286–292

    Article  Google Scholar 

  4. Khazali AH, Kalantar M (2011) Optimal reactive power dispatch based on harmony search algorithm. Int J Electr Power Energy Syst 33(3):684–692

    Article  Google Scholar 

  5. Sulaiman MH, Ing WL, Mustaffa Z, Mohamed MR (2015) Grey wolf optimizer for solving economic dispatch problem with valve-loading effects. ARPN J Eng Appl Sci 10(21):9796–9801

    Google Scholar 

  6. Sulaiman MH, Mustaffa Z, Rashid MIM, Daniyal H (2018) Economic dispatch solution using moth-flame optimization algorithm. In: MATEC web of conferences, vol 214

    Google Scholar 

  7. Park J-B, Jeong Y-W, Lee W-N, Shin J-R (2008) An improved particle swarm optimization for economic dispatch problems with non-smooth cost functions, 20(1):7

    Google Scholar 

  8. Chen P-H, Chang H-C (2002) Large-scale economic dispatch by genetic algorithm. IEEE Trans Power Syst 10(4):1919–1926

    Article  Google Scholar 

  9. Taha IBM, Elattar EE (2018) Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser. IET Gener Transm Distrib 12(14):3421–3434

    Article  Google Scholar 

  10. Attia AF, El Sehiemy RA, Hasanien HM (2018) Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int J Electr Power Energy Syst 99(January):331–343

    Article  Google Scholar 

  11. Duman S, Güvenç U, Sönmez Y, Yörükeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59:86–95

    Article  Google Scholar 

  12. Abido MA (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24(7):563–571

    Article  Google Scholar 

  13. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  14. Sulaiman MH, Ing WL, Mustaffa Z, Mohamed MR (2015) Grey wolf optimizer for solving economic dispatch problem with valve-loading effects ARPN. J Eng Appl Sci 10(21):1619–1628

    Google Scholar 

  15. Abdel-Fatah S, Ebeed M, Kamel S (2019) Optimal reactive power dispatch using modified sine cosine algorithm. In: Proceedings of 2019 international conference on innovation trends computer engineering, ITCE 2019, no February, pp 510–514

    Google Scholar 

  16. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  17. Ng Shin Mei R, Sulaiman MH, Mustaffa Z, Daniyal H (2017) Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique. Appl Soft Comput J 59:210–222

    Article  Google Scholar 

  18. Lee KY, Park YM, Ortiz JL (1985) A united approach to optimal real and reactive power dispatch. IEEE Power Eng Rev PER-5(5):42–43

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the University Malaysia Pahang (UMP) and the Ministry of Higher Education Malaysia (MOHE) under Fundamental Research Grant Scheme FRGS/1/2017/TK04/UMP/03/1 & RDU170129.

Author information

Authors and Affiliations

  1. Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    Salman Alabd, Mohd Herwan Sulaiman & Muhammad Ikram Mohd Rashid

Authors
  1. Salman Alabd
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  2. Mohd Herwan Sulaiman
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  3. Muhammad Ikram Mohd Rashid
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Corresponding authors

Correspondence to Mohd Herwan Sulaiman or Muhammad Ikram Mohd Rashid .

Editor information

Editors and Affiliations

  1. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Zainah Md Zain

  2. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Hamzah Ahmad

  3. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Dwi Pebrianti

  4. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mahfuzah Mustafa

  5. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Nor Rul Hasma Abdullah

  6. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Rosdiyana Samad

  7. Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Maziyah Mat Noh

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Cite this paper

Alabd, S., Sulaiman, M.H., Rashid, M.I.M. (2021). Optimal Power Flow Solutions for Power System Operations Using Moth-Flame Optimization Algorithm. In: Md Zain, Z., et al. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 . NUSYS 2019. Lecture Notes in Electrical Engineering, vol 666. Springer, Singapore. https://doi.org/10.1007/978-981-15-5281-6_15

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