Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)

Umar Attai Abu; Vincent Andrew Akpan; Aghogho Stanley Eyefia

doi:doi:10.11648/j.epes.20261504.12

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| Peer-Reviewed

Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)

Umar Attai Abu

, Vincent Andrew Akpan^*

, Aghogho Stanley Eyefia

Published in American Journal of Electrical Power and Energy Systems (Volume 15, Issue 4)

Received: 13 June 2026 Accepted: 6 August 2026 Published: 20 September 2026

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Abstract

The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental disturbances. The AI-LFC scheme has been proposed for the adaptive control of a well-established Shiroro hydroelectric power generation Station located in Niger State, Nigeria as the case study. This study begins with 3-year data acquisition and experimental data analysis of the key sixteen parameters from the Shiroro Hydroelectric Power Plc in Niger State, Nigeria. Five important output control parameters have been identified to ensure the smooth operation and efficient control of the HPGP to maintain stable grid frequency. Prescribed reference trajectory tracking of the output predictions of the five control parameters as well as the output prediction errors have been used to evaluate the performance of the proposed AI-LFC scheme against that of a well-tuned artificial neural network-based proportional-integral-derivative (NN-based PID) controller for performance comparison purposes. The simulation results show that the proposed AI-LFC scheme outperforms the NN-based PID controller in terms of absolute tracking of the prescribed reference trajectory with absolute zero output predictions errors. The NN-based PID controller exhibits larger output prediction errors, overshoots, non-minimum and oscillatory behaviours, and require significant amount of control efforts to track the desired reference trajectory with occasional inability to reach the desired prescribed reference trajectory. The proposed AI-LFC scheme has been successfully formulated, implemented and validated for the modeling and control of the Shiroro hydroelectric power generation plant as a case study. The proposed AI-LFC outperforms a well-tuned NN-based PID controller and offers promising optimal adaptive control potentials that could be adapted for direct modeling and adaptive control of renewable systems.

Published in	American Journal of Electrical Power and Energy Systems (Volume 15, Issue 4)
DOI	10.11648/j.epes.20261504.12
Page(s)	79-102
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

Adaptive Intelligent Load Frequency Control, Adaptive Neuro-Fuzzy Inference System, Artificial Neural Networks, Fuzzy Logic Controller, Hydroelectric Power Generation Plant

References

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Abu, U. A., Akpan, V. A., Eyefia, A. S. (2026). Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria). American Journal of Electrical Power and Energy Systems, 15(4), 79-102. https://doi.org/10.11648/j.epes.20261504.12

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Abu, U. A.; Akpan, V. A.; Eyefia, A. S. Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria). Am. J. Electr. Power Energy Syst. 2026, 15(4), 79-102. doi: 10.11648/j.epes.20261504.12

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Abu UA, Akpan VA, Eyefia AS. Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria). Am J Electr Power Energy Syst. 2026;15(4):79-102. doi: 10.11648/j.epes.20261504.12

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@article{10.11648/j.epes.20261504.12,
  author = {Umar Attai Abu and Vincent Andrew Akpan and Aghogho Stanley Eyefia},
  title = {Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {15},
  number = {4},
  pages = {79-102},
  doi = {10.11648/j.epes.20261504.12},
  url = {https://doi.org/10.11648/j.epes.20261504.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20261504.12},
  abstract = {The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental disturbances. The AI-LFC scheme has been proposed for the adaptive control of a well-established Shiroro hydroelectric power generation Station located in Niger State, Nigeria as the case study. This study begins with 3-year data acquisition and experimental data analysis of the key sixteen parameters from the Shiroro Hydroelectric Power Plc in Niger State, Nigeria. Five important output control parameters have been identified to ensure the smooth operation and efficient control of the HPGP to maintain stable grid frequency. Prescribed reference trajectory tracking of the output predictions of the five control parameters as well as the output prediction errors have been used to evaluate the performance of the proposed AI-LFC scheme against that of a well-tuned artificial neural network-based proportional-integral-derivative (NN-based PID) controller for performance comparison purposes. The simulation results show that the proposed AI-LFC scheme outperforms the NN-based PID controller in terms of absolute tracking of the prescribed reference trajectory with absolute zero output predictions errors. The NN-based PID controller exhibits larger output prediction errors, overshoots, non-minimum and oscillatory behaviours, and require significant amount of control efforts to track the desired reference trajectory with occasional inability to reach the desired prescribed reference trajectory. The proposed AI-LFC scheme has been successfully formulated, implemented and validated for the modeling and control of the Shiroro hydroelectric power generation plant as a case study. The proposed AI-LFC outperforms a well-tuned NN-based PID controller and offers promising optimal adaptive control potentials that could be adapted for direct modeling and adaptive control of renewable systems.},
 year = {2026}
}

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TY  - JOUR
T1  - Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)
AU  - Umar Attai Abu
AU  - Vincent Andrew Akpan
AU  - Aghogho Stanley Eyefia
Y1  - 2026/09/20
PY  - 2026
N1  - https://doi.org/10.11648/j.epes.20261504.12
DO  - 10.11648/j.epes.20261504.12
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 79
EP  - 102
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20261504.12
AB  - The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental disturbances. The AI-LFC scheme has been proposed for the adaptive control of a well-established Shiroro hydroelectric power generation Station located in Niger State, Nigeria as the case study. This study begins with 3-year data acquisition and experimental data analysis of the key sixteen parameters from the Shiroro Hydroelectric Power Plc in Niger State, Nigeria. Five important output control parameters have been identified to ensure the smooth operation and efficient control of the HPGP to maintain stable grid frequency. Prescribed reference trajectory tracking of the output predictions of the five control parameters as well as the output prediction errors have been used to evaluate the performance of the proposed AI-LFC scheme against that of a well-tuned artificial neural network-based proportional-integral-derivative (NN-based PID) controller for performance comparison purposes. The simulation results show that the proposed AI-LFC scheme outperforms the NN-based PID controller in terms of absolute tracking of the prescribed reference trajectory with absolute zero output predictions errors. The NN-based PID controller exhibits larger output prediction errors, overshoots, non-minimum and oscillatory behaviours, and require significant amount of control efforts to track the desired reference trajectory with occasional inability to reach the desired prescribed reference trajectory. The proposed AI-LFC scheme has been successfully formulated, implemented and validated for the modeling and control of the Shiroro hydroelectric power generation plant as a case study. The proposed AI-LFC outperforms a well-tuned NN-based PID controller and offers promising optimal adaptive control potentials that could be adapted for direct modeling and adaptive control of renewable systems.
VL  - 15
IS  - 4
ER  -

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Umar Attai Abu

Department of Electrical and Electronics Engineering, The Federal Polytechnic, Idah, Nigeria

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http://orcid.org/0009-0009-3573-4921
Vincent Andrew Akpan

Department of Biomedical Engineering, The Federal University of Technology, Akure, Nigeria

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http://orcid.org/0000-0002-5155-2863
Aghogho Stanley Eyefia

Verteville Energy Company Limited, EurafricSapele Power Station, Sapele, Nigeria

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http://orcid.org/0000-0001-7044-0325

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Abu, U. A., Akpan, V. A., Eyefia, A. S. (2026). Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria). American Journal of Electrical Power and Energy Systems, 15(4), 79-102. https://doi.org/10.11648/j.epes.20261504.12

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Abu, U. A.; Akpan, V. A.; Eyefia, A. S. Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria). Am. J. Electr. Power Energy Syst. 2026, 15(4), 79-102. doi: 10.11648/j.epes.20261504.12

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Abu UA, Akpan VA, Eyefia AS. Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria). Am J Electr Power Energy Syst. 2026;15(4):79-102. doi: 10.11648/j.epes.20261504.12

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@article{10.11648/j.epes.20261504.12,
  author = {Umar Attai Abu and Vincent Andrew Akpan and Aghogho Stanley Eyefia},
  title = {Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {15},
  number = {4},
  pages = {79-102},
  doi = {10.11648/j.epes.20261504.12},
  url = {https://doi.org/10.11648/j.epes.20261504.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20261504.12},
  abstract = {The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental disturbances. The AI-LFC scheme has been proposed for the adaptive control of a well-established Shiroro hydroelectric power generation Station located in Niger State, Nigeria as the case study. This study begins with 3-year data acquisition and experimental data analysis of the key sixteen parameters from the Shiroro Hydroelectric Power Plc in Niger State, Nigeria. Five important output control parameters have been identified to ensure the smooth operation and efficient control of the HPGP to maintain stable grid frequency. Prescribed reference trajectory tracking of the output predictions of the five control parameters as well as the output prediction errors have been used to evaluate the performance of the proposed AI-LFC scheme against that of a well-tuned artificial neural network-based proportional-integral-derivative (NN-based PID) controller for performance comparison purposes. The simulation results show that the proposed AI-LFC scheme outperforms the NN-based PID controller in terms of absolute tracking of the prescribed reference trajectory with absolute zero output predictions errors. The NN-based PID controller exhibits larger output prediction errors, overshoots, non-minimum and oscillatory behaviours, and require significant amount of control efforts to track the desired reference trajectory with occasional inability to reach the desired prescribed reference trajectory. The proposed AI-LFC scheme has been successfully formulated, implemented and validated for the modeling and control of the Shiroro hydroelectric power generation plant as a case study. The proposed AI-LFC outperforms a well-tuned NN-based PID controller and offers promising optimal adaptive control potentials that could be adapted for direct modeling and adaptive control of renewable systems.},
 year = {2026}
}

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TY  - JOUR
T1  - Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)
AU  - Umar Attai Abu
AU  - Vincent Andrew Akpan
AU  - Aghogho Stanley Eyefia
Y1  - 2026/09/20
PY  - 2026
N1  - https://doi.org/10.11648/j.epes.20261504.12
DO  - 10.11648/j.epes.20261504.12
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 79
EP  - 102
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20261504.12
AB  - The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental disturbances. The AI-LFC scheme has been proposed for the adaptive control of a well-established Shiroro hydroelectric power generation Station located in Niger State, Nigeria as the case study. This study begins with 3-year data acquisition and experimental data analysis of the key sixteen parameters from the Shiroro Hydroelectric Power Plc in Niger State, Nigeria. Five important output control parameters have been identified to ensure the smooth operation and efficient control of the HPGP to maintain stable grid frequency. Prescribed reference trajectory tracking of the output predictions of the five control parameters as well as the output prediction errors have been used to evaluate the performance of the proposed AI-LFC scheme against that of a well-tuned artificial neural network-based proportional-integral-derivative (NN-based PID) controller for performance comparison purposes. The simulation results show that the proposed AI-LFC scheme outperforms the NN-based PID controller in terms of absolute tracking of the prescribed reference trajectory with absolute zero output predictions errors. The NN-based PID controller exhibits larger output prediction errors, overshoots, non-minimum and oscillatory behaviours, and require significant amount of control efforts to track the desired reference trajectory with occasional inability to reach the desired prescribed reference trajectory. The proposed AI-LFC scheme has been successfully formulated, implemented and validated for the modeling and control of the Shiroro hydroelectric power generation plant as a case study. The proposed AI-LFC outperforms a well-tuned NN-based PID controller and offers promising optimal adaptive control potentials that could be adapted for direct modeling and adaptive control of renewable systems.
VL  - 15
IS  - 4
ER  -

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