期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 34, 期 6, 页码 4393-4407出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2915283
关键词
PV power station cluster; high PV penetration; high precision dynamic modeling; deep learning; long short-term memory network
资金
- National Key Research and Development Program of China [2016YFB0900404]
- Anhui Electric Power Company
Accurate modeling is an important method for dynamic response analysis and control strategy verification of high photovoltaic (PV) penetration distribution networks. This paper proposes a precise dynamic modeling framework for the two-staged PV station cluster, namely as deep learning clustering hybrid modeling framework. It includes clustering-based equivalent model and error correction model (ECM). A long short-term memory network is used to form the ECM, which models the dynamic response error between the existing equivalent model and the detailed model. The competence of this framework is validated by numerous case studies based on a practical PV cluster construction. The simulation results reveal that the proposed method is featured of low complexity and fast response speed as the equivalent model but has much higher accuracy.
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