Data-Driven Wind Farm Volt/VAR Control Based on Deep Reinforcement Learning
| Autor: | Bolin Zhang, Haotian Liu, Wenchuan Wu, Linlin Liu, Guofeng Zhuang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 020209 energy Rapid construction 020208 electrical & electronic engineering Control (management) Stability (learning theory) Volt 02 engineering and technology Reliability engineering Data-driven 0202 electrical engineering electronic engineering information engineering Reinforcement learning System integration business Voltage |
| Zdroj: | 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). |
| DOI: | 10.1109/ei250167.2020.9347074 |
| Popis: | To eliminate voltage violations and improve the security and economy of wind farms, model-based volt/var optimization has been widely but heavily relies on accurate wind farm models. However, numerous wind farms lack of well maintained models because of rapid construction, limited operation staff and changing environment. It motivates us to develop a data-driven wind farm volt/var control based on deep reinforcement learning (DRL). In order to improve the stability, efficiency and optimality of existing DRL-based methods, entropy-regulation technique is utilized. Numerical simulations not only demonstrate the effectiveness of such data-driven volt/var control method, but also show the superiority of our proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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