Abstract
Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.
Highlights
As one of the renewable sources of energy, wind energy has the advantages of large reserves and little pollution (Vargas et al, 2019)
This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network (CSN)
In order to better explore the characteristics of the ramping database data and improve the prediction accuracy, the feature extraction of power ramping events identified by the optimized spinning door algorithm is carried out (Ronay et al, 2017; Naik et al, 2018)
Summary
As one of the renewable sources of energy, wind energy has the advantages of large reserves and little pollution (Vargas et al, 2019). The randomness and volatility of wind energy in nature, especially ramping events under the influence of extreme weather, bring huge challenges to the planning, and dispatching of power systems (Chun et al, 2009; ZongheGao et al, 2013; Zhang, 2017). The ultra-short-term prediction is mainly used for the control of wind turbines, the short-term prediction is made for the scheduling of the power grid, the medium-term prediction is used for the arrangement of large-scale maintenance, and the long-term prediction is designed for the evaluation of the site selection of the wind farm (Khosravi et al, 2013; Qin, 2018) In principle, it can be divided into physical methods, time series methods, and artificial intelligence methods. Taking the wind power dataset of a certain region in Northwest China as the basis, the feasibility of the proposed model is verified through simulated experiments
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