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Implementation of adaptive critic-based neurocontrollers for turbogenerators in a multimachine power system
G K Venayagamoorthy1, R G Harley, D C Wunsch
1Dept. of Electr. and Comput. Eng., Univ. of Missouri, Rolla, MO, USA.
IEEE Transactions on Neural Networks
|February 5, 2008
Summary
This study introduces optimal neurocontrollers, designed using dual heuristic programming (DHP), to enhance turbogenerator performance in power systems. These advanced controllers demonstrate robustness and stability, outperforming traditional methods even under dynamic conditions.
Area of Science:
- Electrical Engineering
- Control Systems
- Artificial Intelligence
Background:
- Conventional automatic voltage regulators (AVR) and turbine governors face limitations in dynamic multimachine power systems.
- System instability can arise from parameter variations and configuration changes in conventional control systems.
Purpose of the Study:
- To design and implement optimal neurocontrollers for turbogenerators in multimachine power systems.
- To replace conventional automatic voltage regulators (AVR) and turbine governors with advanced neurocontroller solutions.
- To evaluate the real-time performance and robustness of these neurocontrollers.
Main Methods:
- Utilized dual heuristic programming (DHP), a technique within the adaptive critic design (ACD) family, for neurocontroller design.
- Implemented and tested DHP neurocontrollers on the Innovative Integration M67 card with a TMS320C6701 processor.
- Assessed controller performance under various system operating conditions and configurations, including the presence of a power system stabilizer (PSS).
Main Results:
- DHP neurocontrollers demonstrated robustness, maintaining performance unlike conventional controllers during system changes.
- The implemented neurocontrollers proved effective in real-time applications for multiple turbogenerators.
- Continuous online training of neural networks was avoided, mitigating risks of instability.
Conclusions:
- Optimal neurocontrollers based on DHP offer a robust and stable alternative to conventional AVR and turbine governor systems.
- Real-time implementation of these neurocontrollers for multimachine power systems is feasible without continuous online training.
- The proposed neurocontroller design enhances power system stability and performance under dynamic conditions.
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