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A Novel Hybrid Prairie Dog Optimization Algorithm - Marine Predator Algorithm for Tuning Parameters Power System Stabilizer

Journal of Robotics and Control (JRC) · Vol. 4 No. 5 (2023) · hlm. 686–695

DOI
10.18196/jrc.v4i5.19521
ISSN
2715-5072, 2715-5056
Terbit
2023-10-07
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The article presents the parameter tuning of the Power System Stabilizer (PSS) using the hybrid method. The hybrid methods proposed in this article are Praire Dog Optimization (PDO) and Marine Predator Algorithm (MPA). The proposed method can be called PDOMPA. In the PDOMPA method, the marine predator algorithm (MPA) is able to search around optimal individuals when updating population positions. MPA is used to make the exploration and exploitation stages of PDO more valid and accurate. PDO is an algorithm inspired by the life of prairie dogs. Prairie dogs are adapted to colonizing in burrows underground. Prairie dogs have daily habits of eating, observing for predators, establishing fresh burrows, or preserving existing ones. Meanwhile, MPA is a duplication of marine predator life which is modeled mathematically. In order to validate the performance of the PDOMPA method, this article presents a comparative simulation of the objective function and the transient response of PSS. This research uses validation by comparing with conventional methods, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Marine Predator Algorithm (MPA), and Praire Dog Optimization (PDO). Based on the simulation results, PDOMPA presents fast convergence in some cases and shows optimal results compared to competitive algorithms. From the simulation results using load variations, it was found that the proposed method has the ability to reduce the average undershoot and overshoot of speed by 42.2% and 85.37% compared to the PSS-Lead Lag method. Meanwhile the average settling time value of speed is 50.7%.

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  • Universitas Negeri Surabaya
  • Institut Teknologi Sepuluh Nopember
  • Taibah University
  • Al al-Bayt University
  • Universidad de Guadalajara
  • Nigerian Defence Academy
  • Al-Balqa Applied University
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Rekan penulisOrganisasiArtikel bersama
Bambang SupriantoUniversitas Negeri Surabaya7
Ayusta Lukita WardaniUniversitas Negeri Surabaya4
Laith AbualigahAl al-Bayt University4
Diego A. OlivaUniversidad de Guadalajara4
Aliyu SaboNigerian Defence Academy3
Aditya PrapancaUniversitas Negeri Surabaya3
Reza RahmadianUniversitas Negeri Surabaya3
Mahendra WidyartonoUniversitas Negeri Surabaya3
Hisham A. ShehadehAl-Balqa Applied University3
Unit Three KartiniUniversitas Negeri Surabaya2
Heri SuryoatmojoInstitut Teknologi Sepuluh Nopember2
Feby Agung PamujiInstitut Teknologi Sepuluh Nopember2
Abeer A. AljohaniTaibah University2
Joko JokoUniversitas Negeri Surabaya2
Aditya Chandra HermawanUniversitas Negeri Surabaya2
Nur Vidia Laksmi B.Universitas Negeri Surabaya1
Muhammad Syahril MubarokUniversitas Airlangga1
Moh. Zaenal EffendiAfiliasi tidak diketahui1
Tian‐Hua LiuNational Taiwan University of Science and Technology1
Andi Iwan NurhidayatUniversitas Negeri Surabaya1
Muhammad Iskandar JavaUniversitas Negeri Surabaya1
Wahyu Sasongko PutroNanyang Technological University1
Muhammad Roil BilladAfiliasi tidak diketahui1
Dimas Arya Soeadyfa FridyatamaPoliteknik Penerbangan Surabaya1
Toufik MziliChouaib Doukkali University1
Supari MuslimUniversitas Negeri Surabaya1
Tohari AhmadInstitut Teknologi Sepuluh Nopember1
Ricky Eka PutraUniversitas Negeri Surabaya1
Hapsari Peni Agustin TjahyaningtijasUniversitas Negeri Surabaya1
Lusiana AnggrainiUniversitas Negeri Surabaya1
Anita QoiriahUniversitas Negeri Surabaya1
Supari SupariUniversitas Negeri Surabaya1
Dimas Ahmad Nur Kholis SuhermantoUniversitas Negeri Surabaya1
Abeer AljohaniTaibah University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A Novel Hybrid Prairie Dog Optimization Algorithm - Marine Predator Algorithm for Tuning Parameters Power System Stabilizer</dc:title>
	<dc:creator>Aribowo, Widi</dc:creator>
	<dc:creator>Rohman, Miftahur</dc:creator>
	<dc:creator>Baskoro, Farid</dc:creator>
	<dc:creator>Harimurti, Rina</dc:creator>
	<dc:creator>Yamasari, Yuni</dc:creator>
	<dc:creator>Yustanti, Wiyli</dc:creator>
	<dc:subject xml:lang="en-US">Prairie Dog Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Power System Stabilizer</dc:subject>
	<dc:subject xml:lang="en-US">Marine Predator Algorithm</dc:subject>
	<dc:subject xml:lang="en-US">Metaheuristic</dc:subject>
	<dc:subject xml:lang="en-US">Single Machine.</dc:subject>
	<dc:description xml:lang="en-US">The article presents the parameter tuning of the Power System Stabilizer (PSS) using the hybrid method. The hybrid methods proposed in this article are Praire Dog Optimization (PDO) and Marine Predator Algorithm (MPA). The proposed method can be called PDOMPA. In the PDOMPA method, the marine predator algorithm (MPA) is able to search around optimal individuals when updating population positions. MPA is used to make the exploration and exploitation stages of PDO more valid and accurate. PDO is an algorithm inspired by the life of prairie dogs. Prairie dogs are adapted to colonizing in burrows underground. Prairie dogs have daily habits of eating, observing for predators, establishing fresh burrows, or preserving existing ones. Meanwhile, MPA is a duplication of marine predator life which is modeled mathematically. In order to validate the performance of the PDOMPA method, this article presents a comparative simulation of the objective function and the transient response of PSS. This research uses validation by comparing with conventional methods, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Marine Predator Algorithm (MPA), and Praire Dog Optimization (PDO). Based on the simulation results, PDOMPA presents fast convergence in some cases and shows optimal results compared to competitive algorithms. From the simulation results using load variations, it was found that the proposed method has the ability to reduce the average undershoot and overshoot of speed by 42.2% and 85.37% compared to the PSS-Lead Lag method. Meanwhile the average settling time value of speed is 50.7%.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2023-10-07</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/19521</dc:identifier>
	<dc:identifier>10.18196/jrc.v4i5.19521</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 4 No. 5 (2023); 686-695</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v4i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/19521/8650</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Widi Aribowo, Miftahur Rohman, Farid Baskoro, Rina Harimurti, Yuni Yamasari, Wiyli Yustanti</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>