Electric Power ›› 2023, Vol. 56 ›› Issue (10): 96-105.DOI: 10.11930/j.issn.1004-9649.202303050

• Key Technology of Active Support and Operation Control Monitoring of Wind Turbine and Farm • Previous Articles     Next Articles

Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting

Zihan CHEN1(), Wei TENG1(), Xuefeng XU2, Xian DING2, Yibing LIU1   

  1. 1. Key Laboratory of Power Station Energy Transfer, Conversion and System, Ministry of Education, North China Electric Power University, Beijing 102206, China
    2. China Green Development Investment Group Co., Ltd., Beijing 100020, China
  • Received:2023-03-10 Accepted:2023-06-08 Online:2023-10-23 Published:2023-10-28
  • Supported by:
    This work is supported by National Natural Science Foundation of China (Intelligent Fault Diagnosis and Life Prediction of Wind Turbine Group under Semi-Supervised Environment, No.51775186).

Abstract:

In order to make full use of the prior relationships among data features and improve the prediction accuracy of medium and long term wind power at wind farms, a medium and long term wind power prediction model based on graph convolution neural network (GCN), wind velocity differential fitting (DF), and particle swarm optimization (PSO) is proposed. By analyzing the whole process of wind power generation, the influencing factors of wind power and the interrelation among them are explored, and the GCN model is built. The wind velocity and power utilization efficiency are fitted respectively. The wind power is calculated by combining with the wind velocity–power calculation model based on DF. The loss of the model includes three parts: power loss, wind velocity loss and power utilization efficiency loss. PSO algorithm is used to determine the appropriate weight for the three losses. The on-site examples of two wind farms show that the relative root mean square error of the wind power prediction model in the next 10 days is 11.44% and 13.09%, respectively, which has a high prediction accuracy.

Key words: wind power generation, wind power prediction, graph convolutional neural network, wind velocity differential fitting, particle swarm optimization algorithm