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

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 state of the wind turbine and the loss in the station, 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. In addition, the graph convolution network (GCN) is employed to extract the spatial relationship between neighboring wind turbines based on the topology and patterns of historical data. The case studies are performed using actual data from a wind farm in northern China. The study results indicate that the computation error using the proposed model is lower than that using the conventional physics-based methods and is also lower than that using other artificial intelligence methods.