Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (12): 36-41.doi: 10.16180/j.cnki.issn1007-7820.2021.12.007

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Estimation Model of Wind Power Reserve Capacity Based on PSO-BP Neural Network

ZHU Chengming,WEI Yunbing,JIANG Chengcheng,ZHU Jian'an   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2020-08-06 Online:2021-12-15 Published:2021-12-06
  • Supported by:
    National Natural Science Foundation of China(51507157)


In view of the effect of the volatility and randomness of wind power on the dispatching of the power grid, a neural network-based wind power reserve capacity estimation model is proposed. In this model, the weights of each link layer in BP neural network are optimized by particle swarm optimization algorithm to improve the prediction of wind power value in the future. The Pearson correlation coefficient is used to extract the influence factors which are positively correlated with the prediction error, and then the multiple linear regression method is used to associate the extracted influencing factors to calculate the reserve capacity of wind power. The simulation results show that 80% of the prediction error is within the estimation range of the model, further verifying the effectiveness of the model.

Key words: wind power generation, wind power, neural network, particle swarm optimization algorithm, prediction error, Pearson correlation coefficient, multiple linear regression, reserve capacity

CLC Number: 

  • TP393