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- Title
基于隐式最大似然估计的风电出力场景生成.
- Authors
廖文龙; 任 翔; 杨 哲; 杨文清; 魏 超
- Abstract
With the increasing penetration of wind power,how to effectively describe the uncertainty of wind power output has become a huge challenge for the operation and planning of distribution network,for which,a scenario generation method of wind power output is proposed based on implicit maximum likelihood estimation. According to the data characteristics of wind power output curves,the loss function and network structure suitable for scenario generation of wind power output are designed. Through unsupervised training, the scenario generator can learn the mapping relationship between Gaussian noise and wind power output scenarios. The wind power output scenarios with different time scales can be generated with the proposed method by only adjusting the relevant parameters in the model. The simulative results show that both the forecasting interval average width and forecasting interval coverage percentage of the proposed method are better than those of the existing generative adversarial network,and the proposed method has certain univer‐ sality for different wind farms.
- Subjects
WIND power; MAXIMUM likelihood statistics; WIND power plants; RANDOM noise theory; DISTRIBUTION planning; PROBABILISTIC generative models; PERCENTILES
- Publication
Electric Power Automation Equipment / Dianli Zidonghua Shebei, 2022, Vol 42, Issue 11, p56
- ISSN
1006-6047
- Publication type
Article
- DOI
10.16081/j.epae.202205006