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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) · hlm. 804–820

DOI
10.18196/jrc.v5i3.21347
ISSN
2715-5072, 2715-5056
Terbit
2024-04-23
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng

Abstrak

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.

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  • Universitas Negeri Surabaya
  • Institut Teknologi Sepuluh Nopember
  • National Research and Innovation Agency
  • Taibah University
  • Al al-Bayt University
  • Universidad de Guadalajara
  • Nigerian Defence Academy
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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
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
Toufik MziliChouaib Doukkali University1
Supari MuslimUniversitas Negeri Surabaya1
Supari SupariUniversitas Negeri Surabaya1
Wiyli YustantiUniversitas Negeri Surabaya1
Farid BaskoroUniversitas Negeri Surabaya1
Yuni YamasariUniversitas Negeri Surabaya1
Miftahur RohmanUniversitas Negeri Surabaya1
Rina HarimurtiUniversitas Negeri Surabaya1
Dimas Ahmad Nur Kholis SuhermantoUniversitas Negeri Surabaya1
Taurista Perdana SyawitriNational Research and Innovation Agency1
Ganesha Tri ChandrasaInstitut Teknologi Sepuluh Nopember1
Barman TambunanNational Research and Innovation Agency1
Tata SutardiNational Research and Innovation Agency1
Adel A. ElbasetMinia University1
Alwy Muhammad RaviTelkom University1
Dedet Candra RiawanInstitut Teknologi Sepuluh Nopember1
Bambang SupriantoUniversitas Negeri Surabaya1
Abeer AljohaniTaibah University1
Subuh Isnur HaryudoUniversitas Negeri Surabaya1
Fendi AchmadUniversitas Negeri Surabaya1

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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">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>