Araştırma Makalesi
BibTex RIS Kaynak Göster

A MODIFIED SALP SWARM OPTIMIZATION ALGORITHM BASED ON THE LOAD FREQUENCY CONTROL OF MULTIPLE-SOURCE POWER SYSTEM

Yıl 2023, , 73 - 84, 30.06.2023
https://doi.org/10.53600/ajesa.1321186

Öz

This work proposes a modified Salp Swarm Optimization Algorithm (SSA) for addressing a multi-source power state's Load Frequency Control (LFC). A controller parameter tuning of the SSA method and its application to the LFC of a multi-source power system with several power generating sources. Derive to the controller parameters, a single area telecommunications device that permits two power system with integrated controlles according to each unit is considered first, and the SSA approach is used. The tunned SSA algorithm is used to optimize the integral (I), proportional integral (PI), and proportional integral derivative (PID) parameters. The research is expanded to include a multi-area multi-source power system, as well as an HVDC link is proposed for connectivity of two regions in addition to the current AC point of intersection. This same tunned SSA method is used to improve the parameters of the Integral (I), Proportional Integral (PI), and Proportional - integral - derivative Derivative (PID). Consequently, the suggested system is shown to be resilient and unaffected by changes of the loading situation, system parameters, or SLP size.

Kaynakça

  • Kundur P. Power system stability and control. New York: Mc-Graw Hill; 1994.
  • Elgerd OI. Electric energy systems theory an introduction. New Delhi: Tata McGraw-Hill; 1983.
  • Hassan B. Robust power system frequency control. New York: Springer; 2009.
  • Mohanty, B., Panda, S., & Hota, P. K. (2014). Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. International journal of electrical power & energy systems, 54, 77-85.
  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in engineering software, 114, 163-191. McCauley, D. J., Pinsky, M. L., Palumbi, S. R., Estes, J. A., Joyce, F. H., & Warner, R. R. (2015). Marine defaunation: Animal loss in the global ocean. Science, 347(6219), 1255641
  • Abualigah L., Shehab M., Mohammad Al Shinwan, H. M. A. (2019). „salp swarm algorithm comprehensive survey. Neural Computing and Applications. 8 - M. L., Aspects of jet propulsion in salps.,‖ Can J Zool 68765–77., 1990
  • Shehab, M., Khader, A. T., & Al-Betar, M. (2016). New selection schemes for particle swarm optimization. IEEJ Transactions on Electronics, Information and Systems, 136(12), 1706-1711
  • Rizk-Allah, R. M., Hassanien, A. E., Elhoseny, M., & Gunasekaran, M. (2019). A new binary salp swarm algorithm: development and application for optimization tasks. Neural Computing and Applications, 31, 1641-1663
  • Abusnaina M., Ahmed A. Ahmad, Sobhi Jarrar, Radi Mafarja, “Training neural networksusing Salp Swarm Algorithm for pattern classification, ACM,‖ Int. Conf. Proceeding Ser.,2018.
  • Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy conversion and management, 179, 362-372.
  • Anderson, P. A. V., & Bone, Q. (1980). Communication between individuals in salp chains. II. Physiology. Proceedings of the Royal Society of London. Series B. Biological Sciences, 210(1181), 559-574.
  • Andersen, V., & Nival, P. (1986). A model of the population dynamics of salps in coastal waters of the Ligurian Sea. Journal of plankton research, 8(6), 1091-1110.
  • Henschke, N., Smith, J. A., Everett, J. D., & Suthers, I. M. (2015). Population drivers of a Thalia democratica swarm: insights from population modelling. Journal of Plankton Research, 37(5), 1074-1087.
  • Mirjalili, H. F. (2019) Salp Swarm Algorithm: Theory, Literature Review, and Application inExtreme Learning Machines ,‖ Nature-Inspired Optim. pp 185-199.
  • Shehab, M., Khader, A. T., & Laouchedi, M. (2018). Modified cuckoo search algorithm for solving global optimization problems. In Recent Trends in Information and Communication Technology: Proceedings of the 2nd International Conference of Reliable Information and Communication Technology (IRICT 2017) (pp. 561-570). Springer International Publishing.
  • Ibrahim, R. A., Ewees, A. A., Oliva, D., Abd Elaziz, M., & Lu, S. (2019). Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing, 10, 3155-3169.
  • Abusnaina, A. A., Ahmad, S., Jarrar, R., & Mafarja, M. (2018, June). Training neural networks using salp swarm algorithm for pattern classification. In Proceedings of the 2nd international conference on future networks and distributed systems (pp. 1-6).
  • Achelia, D. (2018, November). A chaotic binary salp swarm algorithm for solving the graph coloring problem. In Modelling and Implementation of Complex Systems: Proceedings of the 5th International Symposium, MISC 2018, December 16-18, 2018, Laghouat, Algeria (Vol. 64, p. 106). Springer
  • Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy conversion and management, 179, 362-372.
Yıl 2023, , 73 - 84, 30.06.2023
https://doi.org/10.53600/ajesa.1321186

Öz

Kaynakça

  • Kundur P. Power system stability and control. New York: Mc-Graw Hill; 1994.
  • Elgerd OI. Electric energy systems theory an introduction. New Delhi: Tata McGraw-Hill; 1983.
  • Hassan B. Robust power system frequency control. New York: Springer; 2009.
  • Mohanty, B., Panda, S., & Hota, P. K. (2014). Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. International journal of electrical power & energy systems, 54, 77-85.
  • Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in engineering software, 114, 163-191. McCauley, D. J., Pinsky, M. L., Palumbi, S. R., Estes, J. A., Joyce, F. H., & Warner, R. R. (2015). Marine defaunation: Animal loss in the global ocean. Science, 347(6219), 1255641
  • Abualigah L., Shehab M., Mohammad Al Shinwan, H. M. A. (2019). „salp swarm algorithm comprehensive survey. Neural Computing and Applications. 8 - M. L., Aspects of jet propulsion in salps.,‖ Can J Zool 68765–77., 1990
  • Shehab, M., Khader, A. T., & Al-Betar, M. (2016). New selection schemes for particle swarm optimization. IEEJ Transactions on Electronics, Information and Systems, 136(12), 1706-1711
  • Rizk-Allah, R. M., Hassanien, A. E., Elhoseny, M., & Gunasekaran, M. (2019). A new binary salp swarm algorithm: development and application for optimization tasks. Neural Computing and Applications, 31, 1641-1663
  • Abusnaina M., Ahmed A. Ahmad, Sobhi Jarrar, Radi Mafarja, “Training neural networksusing Salp Swarm Algorithm for pattern classification, ACM,‖ Int. Conf. Proceeding Ser.,2018.
  • Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy conversion and management, 179, 362-372.
  • Anderson, P. A. V., & Bone, Q. (1980). Communication between individuals in salp chains. II. Physiology. Proceedings of the Royal Society of London. Series B. Biological Sciences, 210(1181), 559-574.
  • Andersen, V., & Nival, P. (1986). A model of the population dynamics of salps in coastal waters of the Ligurian Sea. Journal of plankton research, 8(6), 1091-1110.
  • Henschke, N., Smith, J. A., Everett, J. D., & Suthers, I. M. (2015). Population drivers of a Thalia democratica swarm: insights from population modelling. Journal of Plankton Research, 37(5), 1074-1087.
  • Mirjalili, H. F. (2019) Salp Swarm Algorithm: Theory, Literature Review, and Application inExtreme Learning Machines ,‖ Nature-Inspired Optim. pp 185-199.
  • Shehab, M., Khader, A. T., & Laouchedi, M. (2018). Modified cuckoo search algorithm for solving global optimization problems. In Recent Trends in Information and Communication Technology: Proceedings of the 2nd International Conference of Reliable Information and Communication Technology (IRICT 2017) (pp. 561-570). Springer International Publishing.
  • Ibrahim, R. A., Ewees, A. A., Oliva, D., Abd Elaziz, M., & Lu, S. (2019). Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing, 10, 3155-3169.
  • Abusnaina, A. A., Ahmad, S., Jarrar, R., & Mafarja, M. (2018, June). Training neural networks using salp swarm algorithm for pattern classification. In Proceedings of the 2nd international conference on future networks and distributed systems (pp. 1-6).
  • Achelia, D. (2018, November). A chaotic binary salp swarm algorithm for solving the graph coloring problem. In Modelling and Implementation of Complex Systems: Proceedings of the 5th International Symposium, MISC 2018, December 16-18, 2018, Laghouat, Algeria (Vol. 64, p. 106). Springer
  • Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy conversion and management, 179, 362-372.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Anas Mahdi Al-zubaıdı Bu kişi benim 0000-0002-7034-8980

Galip Cansever Bu kişi benim 0000-0003-2294-4259

Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 14 Nisan 2022
Kabul Tarihi 12 Haziran 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Al-zubaıdı, A. M., & Cansever, G. (2023). A MODIFIED SALP SWARM OPTIMIZATION ALGORITHM BASED ON THE LOAD FREQUENCY CONTROL OF MULTIPLE-SOURCE POWER SYSTEM. AURUM Journal of Engineering Systems and Architecture, 7(1), 73-84. https://doi.org/10.53600/ajesa.1321186

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