Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm

Shuhangi Sambariya; Mahendra Lalwani; Dhanesh Kumar Sambariya

doi:doi:10.11648/j.ajece.20261001.12

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Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm

Shuhangi Sambariya^*

, Mahendra Lalwani

, Dhanesh Kumar Sambariya

Published in American Journal of Electrical and Computer Engineering (Volume 10, Issue 1)

Received: 14 June 2026 Accepted: 22 July 2026 Published: 25 August 2026

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Abstract

Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To achieve effective frequency regulation under load disturbances, a decentralized control strategy employing three Proportional-Integral (PI) controllers is adopted. Among these, one PI controller is commonly shared by all generating units, while the remaining two controllers are independently assigned to the Thermal and Gas generating units to improve their dynamic response. The optimal tuning of the PI controller parameters is formulated as an optimization problem and solved using the Sailfish Optimization (SFO) algorithm, a recent nature-inspired metaheuristic optimization technique known for its excellent exploration and exploitation capabilities. The Integral Square Error (ISE) is selected as the objective function to minimize frequency deviations and enhance the overall dynamic performance of the power system. The effectiveness of the proposed SFO-based PI controller is evaluated through comprehensive time-domain simulations under step load perturbations. The system performance is assessed using transient response characteristics, including maximum overshoot, rise time, settling time,together with standard performance indices and eigenvalue analysis to examine system stability. The simulation results demonstrate that the proposed SFO-PI controller significantly improves frequency regulation, reduces oscillations, and achieves faster settling with lower performance index values compared with conventional PI tuning approaches and other optimization-based controllers reported in the literature. Furthermore, eigenvalue analysis confirms enhanced damping characteristics and improved stability of the proposed control scheme. The results establish that the SFO provides an effective and reliable framework for optimal controller design in isolated multi-source power systems.

Published in	American Journal of Electrical and Computer Engineering (Volume 10, Issue 1)
DOI	10.11648/j.ajece.20261001.12
Page(s)	19-34
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Isolated Multi Source Power System, Load Frequency Control, Sailfish Optimization Algorithm, PI Controller, Integral Square Error

References

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[2]	Y. L. Karnavas and E. Nivolianiti, "Harris hawks optimization algorithm for load frequency control of isolated multi-source power generating systems," International Journal of Emerging Electric Power Systems, vol. 25, no. 4, pp. 555-571, 2024. https://doi.org/10.1515/ijeeps-2023-0035
[3]	D. K. Sambariya, P. Jangid, and S. Sambariya, "Optimal design of load frequency controller for a single area system using fire fly algorithm," in 2023 IEEE Renewable Energy and Sustainable E-Mobility Conference (RESEM), 2023, pp. 1-6. https://doi.org/10.1109/RESEM57584.2023.10236160
[4]	K. S. S. Ramakrhttpishna and T. S. Bhatti, "Sampled-data automatic load frequency control of a single area power system with multi-source power generation," Electric Power Components and Systems, vol. 35, no. 8, pp. 955-980, 2007. https://doi.org/10.1080/15325000701199479
[5]	K. Godara and N. Kumar, "Frequency analysis of multi-source single area power system using WCA algorithm," in 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), 2021, pp. 492-496. https://doi.org/10.1109/RDCAPE52977.2021.9633690
[6]	N. Paliwal, L. Srivastava, and M. Pandit, "Application of grey wolf optimization algorithm for load frequency control in multi-source single area power system," Evolutionary Intelligence, vol. 15, no. 1, pp. 563-584, 2022. https://doi.org/10.1007/s12065-020-00530-5
[7]	B. K. Sahu, T. K. Pati, J. R. Nayak, S. Panda, and S. K. Kar, "A novel hybrid LUS-TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system," International Journal of Electrical Power & Energy Systems, vol. 74, pp. 58-69, 2016. newpage
[8]	S. Sambariya, M. Lalwani, and D. K. Sambariya, "Effective design of load frequency controller for a single area system using sailfish optimization algorithm," in 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, 2025, pp. 1-8. https://doi.org/10.1109/OTCON65728.2025.11070909
[9]	D. Dinakin and P. Oluseyi, "Comparative study of meta-heuristic and classical techniques for automatic generation control of a multi-source single-area hydro-thermal-gas power system," Kufa Journal of Engineering, vol. 10, no. 3, pp. 151-172, 2021. https://doi.org/10.30572/2018/KJE/100310
[10]	R. Sivalingam, S. Chinnamuthu, and S. S. Dash, "A hybrid stochastic fractal search and local unimodal sampling based multistage PDF plus (1+PI) controller for automatic generation control of power systems," Journal of the Franklin Institute, vol. 354, no. 12, pp. 4762-4783, 2017.
[11]	S. Padhy, S. Panda, and S. Mahapatra, "A modified GWO technique based cascade PI-PD controller for AGC of power systems in presence of plug in electric vehicles," Engineering Science and Technology, an International Journal, vol. 20, no. 2, pp. 427-442, 2017.
[12]	Y. L. Karnavas and E. Nivolianiti, "Load frequency control in multi-source power generation systems using Harris hawks optimization algorithm," in 2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES), 2022, pp. 1-6. https://doi.org/10.1109/CIEES55704.2022.9990646
[13]	A. Rai and D. K. Das, "Chaotic sailfish optimizer based tilt integral derivative developing controllers to address load frequency control in microgrids with energy storage system," SSRN, 2024. Available: https://ssrn.com/abstract=4967960
[14]	S. Shadravan, H. R. Naji, and V. K. Bardsiri, "The sailfish optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems," Engineering Applications of Artificial Intelligence, vol. 80, pp. 20-34, 2019.
[15]	S. M. Azzam, O. E. Emam, and A. S. Abolaban, "An improved differential evolution with sailfish optimizer (DESFO) for handling feature selection problem," Scientific Reports, vol. 14, no. 1, p. 13517, 2024. https://doi.org/10.1038/s41598-024-63328-w

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APA Style

Sambariya, S., Lalwani, M., Sambariya, D. K. (2026). Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm. American Journal of Electrical and Computer Engineering, 10(1), 19-34. https://doi.org/10.11648/j.ajece.20261001.12

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ACS Style

Sambariya, S.; Lalwani, M.; Sambariya, D. K. Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm. Am. J. Electr. Comput. Eng. 2026, 10(1), 19-34. doi: 10.11648/j.ajece.20261001.12

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AMA Style

Sambariya S, Lalwani M, Sambariya DK. Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm. Am J Electr Comput Eng. 2026;10(1):19-34. doi: 10.11648/j.ajece.20261001.12

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@article{10.11648/j.ajece.20261001.12,
  author = {Shuhangi Sambariya and Mahendra Lalwani and Dhanesh Kumar Sambariya},
  title = {Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {10},
  number = {1},
  pages = {19-34},
  doi = {10.11648/j.ajece.20261001.12},
  url = {https://doi.org/10.11648/j.ajece.20261001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20261001.12},
  abstract = {Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To achieve effective frequency regulation under load disturbances, a decentralized control strategy employing three Proportional-Integral (PI) controllers is adopted. Among these, one PI controller is commonly shared by all generating units, while the remaining two controllers are independently assigned to the Thermal and Gas generating units to improve their dynamic response. The optimal tuning of the PI controller parameters is formulated as an optimization problem and solved using the Sailfish Optimization (SFO) algorithm, a recent nature-inspired metaheuristic optimization technique known for its excellent exploration and exploitation capabilities. The Integral Square Error (ISE) is selected as the objective function to minimize frequency deviations and enhance the overall dynamic performance of the power system. The effectiveness of the proposed SFO-based PI controller is evaluated through comprehensive time-domain simulations under step load perturbations. The system performance is assessed using transient response characteristics, including maximum overshoot,  rise time, settling time,together with standard performance indices  and eigenvalue analysis to examine system stability. The simulation results demonstrate that the proposed SFO-PI controller significantly improves frequency regulation, reduces oscillations, and achieves faster settling with lower performance index values compared with conventional PI tuning approaches and other optimization-based controllers reported in the literature. Furthermore, eigenvalue analysis confirms enhanced damping characteristics and improved stability of the proposed control scheme. The results establish that the SFO provides an effective and reliable framework for optimal controller design in isolated multi-source power systems.},
 year = {2026}
}

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TY  - JOUR
T1  - Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm
AU  - Shuhangi Sambariya
AU  - Mahendra Lalwani
AU  - Dhanesh Kumar Sambariya
Y1  - 2026/08/25
PY  - 2026
N1  - https://doi.org/10.11648/j.ajece.20261001.12
DO  - 10.11648/j.ajece.20261001.12
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 19
EP  - 34
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20261001.12
AB  - Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To achieve effective frequency regulation under load disturbances, a decentralized control strategy employing three Proportional-Integral (PI) controllers is adopted. Among these, one PI controller is commonly shared by all generating units, while the remaining two controllers are independently assigned to the Thermal and Gas generating units to improve their dynamic response. The optimal tuning of the PI controller parameters is formulated as an optimization problem and solved using the Sailfish Optimization (SFO) algorithm, a recent nature-inspired metaheuristic optimization technique known for its excellent exploration and exploitation capabilities. The Integral Square Error (ISE) is selected as the objective function to minimize frequency deviations and enhance the overall dynamic performance of the power system. The effectiveness of the proposed SFO-based PI controller is evaluated through comprehensive time-domain simulations under step load perturbations. The system performance is assessed using transient response characteristics, including maximum overshoot,  rise time, settling time,together with standard performance indices  and eigenvalue analysis to examine system stability. The simulation results demonstrate that the proposed SFO-PI controller significantly improves frequency regulation, reduces oscillations, and achieves faster settling with lower performance index values compared with conventional PI tuning approaches and other optimization-based controllers reported in the literature. Furthermore, eigenvalue analysis confirms enhanced damping characteristics and improved stability of the proposed control scheme. The results establish that the SFO provides an effective and reliable framework for optimal controller design in isolated multi-source power systems.
VL  - 10
IS  - 1
ER  -

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Author Information

Shuhangi Sambariya

Department of Electrical Engineering, Rajasthan Technical University, Kota, India

Contact Email

http://orcid.org/0009-0004-4800-303X
Mahendra Lalwani

Department of Electrical Engineering, Rajasthan Technical University, Kota, India

Contact Email

http://orcid.org/0000-0003-1699-0267
Dhanesh Kumar Sambariya

Department of Electrical Engineering, Rajasthan Technical University, Kota, India

Contact Email

http://orcid.org/0000-0003-0372-1797

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APA Style

Sambariya, S., Lalwani, M., Sambariya, D. K. (2026). Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm. American Journal of Electrical and Computer Engineering, 10(1), 19-34. https://doi.org/10.11648/j.ajece.20261001.12

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ACS Style

Sambariya, S.; Lalwani, M.; Sambariya, D. K. Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm. Am. J. Electr. Comput. Eng. 2026, 10(1), 19-34. doi: 10.11648/j.ajece.20261001.12

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AMA Style

Sambariya S, Lalwani M, Sambariya DK. Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm. Am J Electr Comput Eng. 2026;10(1):19-34. doi: 10.11648/j.ajece.20261001.12

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@article{10.11648/j.ajece.20261001.12,
  author = {Shuhangi Sambariya and Mahendra Lalwani and Dhanesh Kumar Sambariya},
  title = {Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {10},
  number = {1},
  pages = {19-34},
  doi = {10.11648/j.ajece.20261001.12},
  url = {https://doi.org/10.11648/j.ajece.20261001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20261001.12},
  abstract = {Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To achieve effective frequency regulation under load disturbances, a decentralized control strategy employing three Proportional-Integral (PI) controllers is adopted. Among these, one PI controller is commonly shared by all generating units, while the remaining two controllers are independently assigned to the Thermal and Gas generating units to improve their dynamic response. The optimal tuning of the PI controller parameters is formulated as an optimization problem and solved using the Sailfish Optimization (SFO) algorithm, a recent nature-inspired metaheuristic optimization technique known for its excellent exploration and exploitation capabilities. The Integral Square Error (ISE) is selected as the objective function to minimize frequency deviations and enhance the overall dynamic performance of the power system. The effectiveness of the proposed SFO-based PI controller is evaluated through comprehensive time-domain simulations under step load perturbations. The system performance is assessed using transient response characteristics, including maximum overshoot,  rise time, settling time,together with standard performance indices  and eigenvalue analysis to examine system stability. The simulation results demonstrate that the proposed SFO-PI controller significantly improves frequency regulation, reduces oscillations, and achieves faster settling with lower performance index values compared with conventional PI tuning approaches and other optimization-based controllers reported in the literature. Furthermore, eigenvalue analysis confirms enhanced damping characteristics and improved stability of the proposed control scheme. The results establish that the SFO provides an effective and reliable framework for optimal controller design in isolated multi-source power systems.},
 year = {2026}
}

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TY  - JOUR
T1  - Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm
AU  - Shuhangi Sambariya
AU  - Mahendra Lalwani
AU  - Dhanesh Kumar Sambariya
Y1  - 2026/08/25
PY  - 2026
N1  - https://doi.org/10.11648/j.ajece.20261001.12
DO  - 10.11648/j.ajece.20261001.12
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 19
EP  - 34
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20261001.12
AB  - Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To achieve effective frequency regulation under load disturbances, a decentralized control strategy employing three Proportional-Integral (PI) controllers is adopted. Among these, one PI controller is commonly shared by all generating units, while the remaining two controllers are independently assigned to the Thermal and Gas generating units to improve their dynamic response. The optimal tuning of the PI controller parameters is formulated as an optimization problem and solved using the Sailfish Optimization (SFO) algorithm, a recent nature-inspired metaheuristic optimization technique known for its excellent exploration and exploitation capabilities. The Integral Square Error (ISE) is selected as the objective function to minimize frequency deviations and enhance the overall dynamic performance of the power system. The effectiveness of the proposed SFO-based PI controller is evaluated through comprehensive time-domain simulations under step load perturbations. The system performance is assessed using transient response characteristics, including maximum overshoot,  rise time, settling time,together with standard performance indices  and eigenvalue analysis to examine system stability. The simulation results demonstrate that the proposed SFO-PI controller significantly improves frequency regulation, reduces oscillations, and achieves faster settling with lower performance index values compared with conventional PI tuning approaches and other optimization-based controllers reported in the literature. Furthermore, eigenvalue analysis confirms enhanced damping characteristics and improved stability of the proposed control scheme. The results establish that the SFO provides an effective and reliable framework for optimal controller design in isolated multi-source power systems.
VL  - 10
IS  - 1
ER  -

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