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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/4/1956 |
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| Summary: | 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. |
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| ISSN: | 2076-3417 |