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Data-driven synchronization of renewable generators
We want to apply a novel data-enabled predictive control (DeePC) algorithm in renewable generators to perform safe and optimal control. The DeePC algorithm is a model-free control approach which provides a possibility to achieve grid synchronization of renewable generators in a data-driven fashion.
Keywords: data-driven control, predictive control, synchronization, power converters, renewables
The massive integration of renewable generators significantly changes the characteristics of modern power systems. Conventional control of renewables is model-based, which can hardly lead to optimal performance in reality because the power grid is ever-changing. Hence, data-driven approaches are preferred as it utilizes the great availability of input/output data to capture the essential dynamics of the power grid, thereby avoiding accurate modeling for the power grid. In this project, we will explore the application of a novel data-enabled predictive control (DeePC) algorithm in renewable generators to achieve grid synchronization in a data-driven fashion, which will possibly achieve better performance than conventional control schemes and ensures strong stability guarantees. We will investigate how to specify the cost function in the predictive control to enforce certain control objectives (e.g., grid synchronization, power regulations, frequency support, etc.), and analyze the robustness and optimality of the approach.
The massive integration of renewable generators significantly changes the characteristics of modern power systems. Conventional control of renewables is model-based, which can hardly lead to optimal performance in reality because the power grid is ever-changing. Hence, data-driven approaches are preferred as it utilizes the great availability of input/output data to capture the essential dynamics of the power grid, thereby avoiding accurate modeling for the power grid. In this project, we will explore the application of a novel data-enabled predictive control (DeePC) algorithm in renewable generators to achieve grid synchronization in a data-driven fashion, which will possibly achieve better performance than conventional control schemes and ensures strong stability guarantees. We will investigate how to specify the cost function in the predictive control to enforce certain control objectives (e.g., grid synchronization, power regulations, frequency support, etc.), and analyze the robustness and optimality of the approach.
1. The student will be introduced to and taught in the concept of power converters (renewable generation) controls and data-enabled predictive control.
2. The student will be introduced to Simulink models of power converters.
3. The student will possibly have access to a real power-electronic converter platform (if she/he is interested).
4. The student will implement the data-driven controller in simulations (and possibly experiments).
5. The student will compare the performance of the controller to the state of the art.
6. The student will investigate the design principle of data-driven synchronization.
7. The student will write the report and prepare a presentation.
This project could be done in person at the Automatic Control Laboratory, hybrid, or completely remotely depending on the current ETH rules. Most importantly, we can change between these forms whenever needed.
The project can be adapted on the run if new interesting research directions arise.
Finally, if the results are promising they can be turned into a publication.
1. The student will be introduced to and taught in the concept of power converters (renewable generation) controls and data-enabled predictive control. 2. The student will be introduced to Simulink models of power converters. 3. The student will possibly have access to a real power-electronic converter platform (if she/he is interested). 4. The student will implement the data-driven controller in simulations (and possibly experiments). 5. The student will compare the performance of the controller to the state of the art. 6. The student will investigate the design principle of data-driven synchronization. 7. The student will write the report and prepare a presentation.
This project could be done in person at the Automatic Control Laboratory, hybrid, or completely remotely depending on the current ETH rules. Most importantly, we can change between these forms whenever needed. The project can be adapted on the run if new interesting research directions arise. Finally, if the results are promising they can be turned into a publication.