Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods
Real-world power systems face challenges from demand fluctuations, system constraints, communication delays, and unmeasurable disturbances. This paper presents a real-time hybrid approach integrating Nonlinear Model Predictive Control (NLMPC) and data-driven methods for automatic generation control...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/4/1956 |
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| author | Ahmed Khamees Hüseyin Altınkaya |
| author_facet | Ahmed Khamees Hüseyin Altınkaya |
| author_sort | Ahmed Khamees |
| collection | DOAJ |
| description | Real-world power systems face challenges from demand fluctuations, system constraints, communication delays, and unmeasurable disturbances. This paper presents a real-time hybrid approach integrating Nonlinear Model Predictive Control (NLMPC) and data-driven methods for automatic generation control (AGC) of synchronous generators, particularly under cyber-physical attacks. Unlike previous studies, this work considers both technical and economic aspects of power system management. A key innovation is the incorporation of a detailed thermo-mechanical model of turbine and governor dynamics, enabling optimized control and effective management of power oscillations. The proposed NLMPC-based AGC strategy addresses governor saturation and generation rate constraints, ensuring stability. Extensive simulations in MATLAB/Simulink, including IEEE 11-bus and 9-bus test systems, validate the controller’s effectiveness in enhancing power system performance under various challenging conditions. |
| format | Article |
| id | doaj-art-e048e08d800a4142bd384de4a53c975d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e048e08d800a4142bd384de4a53c975d2025-08-20T02:44:55ZengMDPI AGApplied Sciences2076-34172025-02-01154195610.3390/app15041956Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven MethodsAhmed Khamees0Hüseyin Altınkaya1Department of Electrical and Electronics Engineering, Karabuk University, Karabuk 78050, TürkiyeDepartment of Electrical and Electronics Engineering, Karabuk University, Karabuk 78050, TürkiyeReal-world power systems face challenges from demand fluctuations, system constraints, communication delays, and unmeasurable disturbances. This paper presents a real-time hybrid approach integrating Nonlinear Model Predictive Control (NLMPC) and data-driven methods for automatic generation control (AGC) of synchronous generators, particularly under cyber-physical attacks. Unlike previous studies, this work considers both technical and economic aspects of power system management. A key innovation is the incorporation of a detailed thermo-mechanical model of turbine and governor dynamics, enabling optimized control and effective management of power oscillations. The proposed NLMPC-based AGC strategy addresses governor saturation and generation rate constraints, ensuring stability. Extensive simulations in MATLAB/Simulink, including IEEE 11-bus and 9-bus test systems, validate the controller’s effectiveness in enhancing power system performance under various challenging conditions.https://www.mdpi.com/2076-3417/15/4/1956automatic generation controlnonlinear model predictive controldata-driven modelsynchronous generators |
| spellingShingle | Ahmed Khamees Hüseyin Altınkaya Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods Applied Sciences automatic generation control nonlinear model predictive control data-driven model synchronous generators |
| title | Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods |
| title_full | Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods |
| title_fullStr | Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods |
| title_full_unstemmed | Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods |
| title_short | Global Optimal Automatic Generation Control of a Multimachine Power System Using Hybrid NLMPC and Data-Driven Methods |
| title_sort | global optimal automatic generation control of a multimachine power system using hybrid nlmpc and data driven methods |
| topic | automatic generation control nonlinear model predictive control data-driven model synchronous generators |
| url | https://www.mdpi.com/2076-3417/15/4/1956 |
| work_keys_str_mv | AT ahmedkhamees globaloptimalautomaticgenerationcontrolofamultimachinepowersystemusinghybridnlmpcanddatadrivenmethods AT huseyinaltınkaya globaloptimalautomaticgenerationcontrolofamultimachinepowersystemusinghybridnlmpcanddatadrivenmethods |