摘要
Abstract
In recent years,there has been a large power supply gap during the period of peak summer electricity consumption to ensure the smooth operation of the power grid in Jiangsu Province,increases the risk of power system frequency instability.Therefore,stable wind power output power has become increasingly important in ensuring power supply work.Considering the randomness and intermittences of wind energy,accurate wind speed prediction can reduce the additional cost of grid-connection for wind power,help the dispatching department of the power system adjust the dispatching plan,improve the wind power consumption and stable operation capability of the power system.This paper proposes a wind speed prediction method using RBF neural network based on AP clustering algorithm(that is"AP-RBF method")from the perspective of improving the accuracy of ultra short term wind speed prediction.Firstly,an AP-RBF model is established,and then the actual wind speed data collected from a wind farm in Jiangsu Province is used as an example for numerical analysis,and the predictive performance of the AP-RBF model is verified.The prediction accuracy and prediction efficiency of various prediction methods are compared and analyzed.The research results show that:1)The AP-RBF method overcomes the sensitivity of traditional clustering methods to initial values by first calculating the clustering results and then calculating the weight matrix for the prediction mode.2)Compared with conventional prediction methods,the AP-RBF method performs the best in overall prediction accuracy and has a faster prediction speed while ensuring the quality of training data.The application of AP-RBF method is of great significance for improving the wind power consumption capacity and frequency stability of power system.关键词
清洁能源/风速/风电/近邻传播聚类算法/径向基函数神经网络/风速预测/精度分析Key words
clean energy/wind speed/wind power/AP clustering algorithm/RBF neural network/wind speed prediction/precision analysis分类
信息技术与安全科学
