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Optimization of load frequency control systems using PSO technique

International Journal of Applied Power Engineering (IJAPE) · Vol. 15 No. 1 (2026) · hlm. 177–185

DOI
10.11591/ijape.v15.i1.pp177-185
ISSN
2722-2624, 2252-8792
Terbit
2026-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This paper investigates the improvement of low-frequency load control (LFC) by optimizing integral part (PID) control using particle swarm optimization (PSO). Load frequency control is important to ensure energy stability by maintaining the balance between production and consumption. Conventional proportional integral derivative controllers are widely used for this purpose; however, their performance can be further improved through optimization. This work uses particle swarm optimization, a nature-inspired algorithm, to set the parameters of the proportional integral derivative controller. PSO was chosen because it can search for good solution space and find a good agreement between control parameters, thus improving the dynamic and stable response of the system. This article provides a comprehensive evaluation of the proposed approach, including simulation results and comparisons with standard PID controllers. The effectiveness of the optimized PID controllers in reducing the frequency difference and improving the overall efficiency of the power plant under different conditions is demonstrated. This study provides insight into the use of artificial intelligence to improve control parameters in the power grid, providing a promising way to improve the efficiency and reliability of frequency controllers.

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  • Woosong University
  • Veer Surendra Sai University of Technology
  • Biju Patnaik University of Technology
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Sivkumar MishraBiju Patnaik University of Technology6
Sanchita SahayAfiliasi tidak diketahui5
Seong-Cheol KimWoosong University4
Papia RayVeer Surendra Sai University of Technology4
Ayush KumarAfiliasi tidak diketahui4
Arun Kumar SahooAfiliasi tidak diketahui4
S. NarasimhaAfiliasi tidak diketahui3
Nisha KediaAfiliasi tidak diketahui3
Raj Alpha SwainAfiliasi tidak diketahui3
Prabin BiswalAfiliasi tidak diketahui3
Swayamjyoti SatapathyAfiliasi tidak diketahui3
Smruti Susmita SahuAfiliasi tidak diketahui3
Aditya Prasad MahapatraAfiliasi tidak diketahui2
Bishweashwar SuklaAfiliasi tidak diketahui2
Harikrishnan K. M.Afiliasi tidak diketahui2
Padarabinda PalaiAfiliasi tidak diketahui2
Sandip Ranjan BeheraAfiliasi tidak diketahui2
Arul Kumar DashAfiliasi tidak diketahui2
Prajna Jeet OjhaAfiliasi tidak diketahui2
Bhabesh RoutrayAfiliasi tidak diketahui2
Nitesh PattanayakAfiliasi tidak diketahui2
Priyansu ShekharAfiliasi tidak diketahui2
Pratyus Ranjan BeheraAfiliasi tidak diketahui2
Priyanka raiAfiliasi tidak diketahui2
Prince KumarAfiliasi tidak diketahui2
Jayanta Kumar SahuAfiliasi tidak diketahui2
Umamani SubudhiAfiliasi tidak diketahui2
Drishana JhunjhunwallaAfiliasi tidak diketahui2
Dashmat HembramAfiliasi tidak diketahui2
Chan-Mook JungWoosong University1
Pankaj SharmaWoosong University1
Monalisa PandaAfiliasi tidak diketahui1
Debani Prashad MishraAfiliasi tidak diketahui1
Sopa Mousumi PatroAfiliasi tidak diketahui1
Kalpa Ranjan BeheraAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijape.iaescore.com/index.php/IJAPE/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Optimization of load frequency control systems using PSO technique</dc:title>
	<dc:creator>Mishra, Debani Prasad</dc:creator>
	<dc:creator>Senapati, Rudranarayan</dc:creator>
	<dc:creator>Yashwanth, Lingam</dc:creator>
	<dc:creator>Uday, Peesodi</dc:creator>
	<dc:creator>Salkuti, Surender Reddy</dc:creator>
	<dc:subject xml:lang="en-US">governor control; load frequency control; particle swarm algorithm; primary control; secondary control</dc:subject>
	<dc:description xml:lang="en-US">This paper investigates the improvement of low-frequency load control (LFC) by optimizing integral part (PID) control using particle swarm optimization (PSO). Load frequency control is important to ensure energy stability by maintaining the balance between production and consumption. Conventional proportional integral derivative controllers are widely used for this purpose; however, their performance can be further improved through optimization. This work uses particle swarm optimization, a nature-inspired algorithm, to set the parameters of the proportional integral derivative controller. PSO was chosen because it can search for good solution space and find a good agreement between control parameters, thus improving the dynamic and stable response of the system. This article provides a comprehensive evaluation of the proposed approach, including simulation results and comparisons with standard PID controllers. The effectiveness of the optimized PID controllers in reducing the frequency difference and improving the overall efficiency of the power plant under different conditions is demonstrated. This study provides insight into the use of artificial intelligence to improve control parameters in the power grid, providing a promising way to improve the efficiency and reliability of frequency controllers.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijape.iaescore.com/index.php/IJAPE/article/view/21615</dc:identifier>
	<dc:identifier>10.11591/ijape.v15.i1.pp177-185</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Applied Power Engineering (IJAPE); Vol 15, No 1: March 2026; 177-185</dc:source>
	<dc:source>2722-2624</dc:source>
	<dc:source>2252-8792</dc:source>
	<dc:source>10.11591/ijape.v15.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijape.iaescore.com/index.php/IJAPE/article/view/21615/13408</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Debani Prasad Mishra, Rudranarayan Senapati, Lingam Yashwanth, Peesodi Uday, Surender Reddy Salkuti</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>