Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control
Fluctuating voltage levels in power grids necessitate automatic voltage regulators (AVRs) to ensure stability. This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control (MPC), which utilizes an extensive mathematical model of the voltage regulat...
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Global Energy Interconnection |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096511725000295 |
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| author | Ebunle Akupan Rene Willy Stephen Tounsi Fokui |
| author_facet | Ebunle Akupan Rene Willy Stephen Tounsi Fokui |
| author_sort | Ebunle Akupan Rene |
| collection | DOAJ |
| description | Fluctuating voltage levels in power grids necessitate automatic voltage regulators (AVRs) to ensure stability. This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control (MPC), which utilizes an extensive mathematical model of the voltage regulation system to optimize the control actions over a defined prediction horizon. This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints, thereby improving stability and performance under dynamic conditions. The findings were compared with those derived from an optimal proportional integral derivative (PID) controller designed using the artificial bee colony (ABC) algorithm. Although the ABC-PID method adjusts the PID parameters based on historical data, it may be difficult to adapt to real-time changes in system dynamics under constraints. Comprehensive simulations assessed both frameworks, emphasizing performance metrics such as disturbance rejection, response to load changes, and resilience to uncertainties. The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation; however, MPC excelled in controlling overshoot and settling time—recording 0.0 % and 0.25 s, respectively. This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior, which exhibited settling times and overshoots exceeding 0.41 s and 5.0 %, respectively. The controllers were implemented using MATLAB/Simulink software, indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations. |
| format | Article |
| id | doaj-art-82caa1fc6ce34c5c8f4c813b5081399e |
| institution | Kabale University |
| issn | 2096-5117 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | Global Energy Interconnection |
| spelling | doaj-art-82caa1fc6ce34c5c8f4c813b5081399e2025-08-20T03:47:41ZengKeAi Communications Co., Ltd.Global Energy Interconnection2096-51172025-04-018226928510.1016/j.gloei.2024.12.003Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive controlEbunle Akupan Rene0Willy Stephen Tounsi Fokui1University of New Hampshire, 105 Main St, Durham NH 03824, United StatesTeleconnect GmbH, Am Lehmberg 54, 01157 Dresden, Germany; Corresponding author.Fluctuating voltage levels in power grids necessitate automatic voltage regulators (AVRs) to ensure stability. This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control (MPC), which utilizes an extensive mathematical model of the voltage regulation system to optimize the control actions over a defined prediction horizon. This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints, thereby improving stability and performance under dynamic conditions. The findings were compared with those derived from an optimal proportional integral derivative (PID) controller designed using the artificial bee colony (ABC) algorithm. Although the ABC-PID method adjusts the PID parameters based on historical data, it may be difficult to adapt to real-time changes in system dynamics under constraints. Comprehensive simulations assessed both frameworks, emphasizing performance metrics such as disturbance rejection, response to load changes, and resilience to uncertainties. The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation; however, MPC excelled in controlling overshoot and settling time—recording 0.0 % and 0.25 s, respectively. This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior, which exhibited settling times and overshoots exceeding 0.41 s and 5.0 %, respectively. The controllers were implemented using MATLAB/Simulink software, indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.http://www.sciencedirect.com/science/article/pii/S2096511725000295Automatic voltage regulationArtificial bee colonyEvolutionary techniquesModel predictive controlPID controllerHydropower |
| spellingShingle | Ebunle Akupan Rene Willy Stephen Tounsi Fokui Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control Global Energy Interconnection Automatic voltage regulation Artificial bee colony Evolutionary techniques Model predictive control PID controller Hydropower |
| title | Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control |
| title_full | Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control |
| title_fullStr | Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control |
| title_full_unstemmed | Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control |
| title_short | Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control |
| title_sort | modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control |
| topic | Automatic voltage regulation Artificial bee colony Evolutionary techniques Model predictive control PID controller Hydropower |
| url | http://www.sciencedirect.com/science/article/pii/S2096511725000295 |
| work_keys_str_mv | AT ebunleakupanrene modelingandcontrolofautomaticvoltageregulationforahydropowerplantusingadvancedmodelpredictivecontrol AT willystephentounsifokui modelingandcontrolofautomaticvoltageregulationforahydropowerplantusingadvancedmodelpredictivecontrol |