AUTHOR=Liu Yu , Huang Kunpeng , Liu Jincheng , Zhang Pei , Liu Zhao 

TITLE=Available power estimation of wind farms based on deep spatio-temporal neural networks

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1032867

DOI=10.3389/fenrg.2023.1032867

ISSN=2296-598X

ABSTRACT=With the development of advanced digital infrastructure in new wind power plants in China. The individual wind turbine level data are available to power operators and can potentially provide more accurate available wind power estimations. In this paper, considering the wind turbine's state and the station's loss, a four-layer Spatio-temporal neural network is proposed to compute the available power of wind farms. Specifically, the long short-term memory (LSTM) network is built for each wind turbine to extract the time-series correlations in historical data. And the graph convolution network (GCN) is employed to extract spatial relationships between neighboring wind turbines based on the topology and historical data patterns. The case studies are performed using actual data from a wind farm in northern China. The study results indicate that the proposed model's computation error is lower than the conventional physics-based methods and is also lower than other artificial intelligence methods.