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Improved Droop Control Based on Modified Osprey Optimization Algorithm in DC Microgrid

Journal of Robotics and Control (JRC) · Vol. 5 No. 3 (2024) · pp. 804–820

DOI
10.18196/jrc.v5i3.21347
ISSN
2715-5072, 2715-5056
Published
2024-04-23
Publisher
Universitas Muhammadiyah Yogyakarta
Language
eng

Abstract

In this research, a modified Osprey optimization algorithm (MOOA) is presented to optimize droop control parameters. MOOA is a modification of the Osprey optimization algorithm by adding levy flight which has the advantage of exploiting a wider space and being adaptive to environmental changes. This research also modifies droop control, Proportional Integral Derivative (PID) is applied to secondary control. PID has flexibility in responding to changes in system conditions and fast response in dealing with system changes. The PID parameters are optimized using MOOA and are called MOOA-PID. The MOOA method is validated using 23 CEC2017 benchmarks-function and performance on DC microgrid systems. This research uses the latest algorithms as a comparison, namely One-to-One Based Optimizer (OOBO), Preschool Educational Optimization Algorithm (PEOA), and the red-tailed hawk (RTH) algorithm in testing 23 CEC2017 benchmark functions. From the simulation of the 23 CEC2017 benchmark function, it is known that the MOOA method has better capabilities. MOOA has advantages in 15 out of 23 benchmark functions. In DC microgrid system testing, MOOA-PID is compared with the Proportional Integral (PI) method which is optimized with MOOA and is called MOOA-PI. Testing on the microgrid is aimed at determining the performance of the transient response of power, voltage and current in the system. Tests on DC microgrid systems found that the application of MOOA-PID in secondary control had better capabilities than MOOA-PI. The average value of voltage overshoot from MOOA-PID is 9.828% better than MOOA-PI. The average ITSE MOOA-PID score is 22.3% better than MOOA-PI.

Keywords

Collaboration network of the authors

40 authors · 121 co-authorship links · 25 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Negeri Surabaya
  • Institut Teknologi Sepuluh Nopember
  • Al al-Bayt University
  • Nigerian Defence Academy
  • Universitas Halu Oleo
  • Universitas Airlangga
  • National Taiwan University of Science and Technology
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bambang SupriantoUniversitas Negeri Surabaya8
Ayusta Lukita WardaniUniversitas Negeri Surabaya4
Laith AbualigahAl al-Bayt University4
Aliyu SaboNigerian Defence Academy3
Diego OlivaAffiliation unknown3
Aditya PrapancaAffiliation unknown3
Reza RahmadianAffiliation unknown3
Mahendra WidyartonoAffiliation unknown3
Hisham A. ShehadehAffiliation unknown3
Unit Three KartiniUniversitas Negeri Surabaya2
Abeer AljohaniAffiliation unknown2
Joko JokoUniversitas Halu Oleo2
Aditya Chandra HermawanAffiliation unknown2
Nur Vidia Laksmi B.Universitas Negeri Surabaya1
Muhammad Syahril MubarokUniversitas Airlangga1
Moh. Zaenal EffendiAffiliation unknown1
Tian‐Hua LiuNational Taiwan University of Science and Technology1
Diego A. OlivaUniversidad de Guadalajara1
Abeer A. AljohaniTaibah University1
Toufik MziliChouaib Doukkali University1
Supari MuslimAffiliation unknown1
Supari SupariAffiliation unknown1
Yuni YamasariAffiliation unknown1
Farid BaskoroUniversitas Negeri Surabaya1
Miftahur RohmanAffiliation unknown1
Rina HarimurtiAffiliation unknown1
Wiyli YustantiAffiliation unknown1
Dimas Ahmad Nur Kholis SuhermantoAffiliation unknown1
Taurista Perdana SyawitriNational Research and Innovation Agency1
Ganesha Tri ChandrasaAffiliation unknown1
Barman TambunanAffiliation unknown1
Tata SutardiAffiliation unknown1
Adel A. ElbasetHeliopolis University1
Alwy Muhammad RaviAffiliation unknown1
Dedet Candra RiawanAffiliation unknown1
Subuh IsnurAffiliation unknown1
Fendi AchmadAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Improved Droop Control Based on Modified Osprey Optimization Algorithm in DC Microgrid</dc:title>
	<dc:creator>Aribowo, Widi</dc:creator>
	<dc:creator>Suryoatmojo, Heri</dc:creator>
	<dc:creator>Pamuji, Feby Agung</dc:creator>
	<dc:subject xml:lang="en-US">Droop Control</dc:subject>
	<dc:subject xml:lang="en-US">Secondary Control</dc:subject>
	<dc:subject xml:lang="en-US">DC Microgrid</dc:subject>
	<dc:subject xml:lang="en-US">Metaheuristic</dc:subject>
	<dc:subject xml:lang="en-US">Optimization.</dc:subject>
	<dc:description xml:lang="en-US">In this research, a modified Osprey optimization algorithm (MOOA) is presented to optimize droop control parameters. MOOA is a modification of the Osprey optimization algorithm by adding levy flight which has the advantage of exploiting a wider space and being adaptive to environmental changes. This research also modifies droop control, Proportional Integral Derivative (PID) is applied to secondary control. PID has flexibility in responding to changes in system conditions and fast response in dealing with system changes. The PID parameters are optimized using MOOA and are called MOOA-PID. The MOOA method is validated using 23 CEC2017 benchmarks-function and performance on DC microgrid systems. This research uses the latest algorithms as a comparison, namely One-to-One Based Optimizer (OOBO), Preschool Educational Optimization Algorithm (PEOA), and the red-tailed hawk (RTH) algorithm in testing 23 CEC2017 benchmark functions. From the simulation of the 23 CEC2017 benchmark function, it is known that the MOOA method has better capabilities. MOOA has advantages in 15 out of 23 benchmark functions. In DC microgrid system testing, MOOA-PID is compared with the Proportional Integral (PI) method which is optimized with MOOA and is called MOOA-PI. Testing on the microgrid is aimed at determining the performance of the transient response of power, voltage and current in the system. Tests on DC microgrid systems found that the application of MOOA-PID in secondary control had better capabilities than MOOA-PI. The average value of voltage overshoot from MOOA-PID is 9.828% better than MOOA-PI. The average ITSE MOOA-PID score is 22.3% better than MOOA-PI.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2024-04-23</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/21347</dc:identifier>
	<dc:identifier>10.18196/jrc.v5i3.21347</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 5 No. 3 (2024); 804-820</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v5i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/21347/9121</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Widi Aribowo, Heri Suryoatmojo, Feby Agung Pamuji</dc:rights>
</oai_dc:dc>