Abstract
During the implementation of active voltage support in wind farms, coordinating the operation of multiple wind turbines presents significant challenges. The dynamic response of the entire wind farm becomes complex during grid faults, making it difficult to achieve coordinated voltage support across different wind turbines. To address this, a coordination control strategy for doubly fed wind farms is here proposed which is based on Q-learning informed by the sensitivity of voltage. First, a method for calculating the voltage sensitivity of DFIG-based wind farms is introduced, utilizing the arbitrary polynomial chaos approach. Additionally, the operational constraints of wind farms are defined based on the average short-circuit ratio of reactive power. The voltage support characteristics of multi-machine wind farms under grid fault conditions are then thoroughly explored. Subsequently, an improved Q-learning algorithm is developed, based on the sensitivity of voltage. This algorithm aids in optimizing the control commands, thus enhancing the effectiveness of the voltage support system. Finally, adopting this voltage sensitivity as the basis for the coordinated control commands and applying the improved Q-learning algorithm as the implementation mechanism, a coordinated control strategy for active voltage support in DFIG-based wind farms is proposed. Simulation results demonstrate that the proposed control strategy can provide effective active voltage support during grid faults.
| Original language | English |
|---|---|
| Article number | 1566923 |
| Journal | Frontiers in Energy Research |
| Volume | 13 |
| DOIs | |
| State | Published - 2025 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 7 Affordable and Clean Energy
Keywords
- DFIG-based Wind farm
- Q-learning
- active voltage support
- coordinated control
- reactive power voltage sensitivity
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