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Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

Journal of Robotics and Control (JRC) · Vol. 4 No. 5 (2023) · hlm. 726–742

DOI
10.18196/jrc.v4i5.19772
ISSN
2715-5072, 2715-5056
Terbit
2023-10-25
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng

Abstrak

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.

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17 penulis · 47 tautan kepenulisan bersama · 33 lintas organisasi

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  • Symbiosis International University
  • Paavai Engineering College
  • The Automotive Research Association of India
  • Muhammadiyah University of Yogyakarta
  • University of Al-Ameed
  • Ministry of Education
  • Imam Ja’afar Al-Sadiq University
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Rekan penulisOrganisasiArtikel bersama
Iswanto IswantoMuhammadiyah University of Yogyakarta2
Shailesh Balkrishna SonawaneSymbiosis International University1
Arundhati Suresh WarkeSymbiosis International University1
Sukrut Shrikant ThipseThe Automotive Research Association of India1
Chetan VarmaThe Automotive Research Association of India1
Preeti WarrierAfiliasi tidak diketahui1
Darmawaty Mohd AliUniversiti Teknologi MARA1
Wan Haszerila Wan HassanUniversiti Teknikal Malaysia Melaka1
Mushtaq Talb TallyMinistry of Education1
Fuqdan A. Al-IbraheemiUniversity of Al-Ameed1
Safwan NadwehImam Ja’afar Al-Sadiq University1
Intisar A.M. Al SayedAfiliasi tidak diketahui1
Karam Samir KhalidAl-Farahidi 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">Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System</dc:title>
	<dc:creator>D., Murugesan</dc:creator>
	<dc:creator>K., Jagatheesan</dc:creator>
	<dc:creator>Shah, Pritesh</dc:creator>
	<dc:creator>Sekhar, Ravi</dc:creator>
	<dc:subject xml:lang="en-US">Genetic Algorithm</dc:subject>
	<dc:subject xml:lang="en-US">Load Frequency Control</dc:subject>
	<dc:subject xml:lang="en-US">Teaching Learning based Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Cohort Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Integral Time Absolute Error</dc:subject>
	<dc:description xml:lang="en-US">Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2023-10-25</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/19772</dc:identifier>
	<dc:identifier>10.18196/jrc.v4i5.19772</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 4 No. 5 (2023); 726-742</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/19772/8683</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Murugesan D, Jagatheesan K, Pritesh Biharilal Shah, Ravi Sekhar</dc:rights>
</oai_dc:dc>