基于量子粒子群优化BP神经网络的风机出力预测

Wind Power Forecasting Based on Optimising BP Neural Network with QPSO

风机的出力具有波动性及间歇性的特性。该特性对风电场并网和调度产生很大影响,然而传统的风机出力预测技术很难达到准确预测的效果。针对上述问题,采用量子粒子群优化BP神经网络的权值和阈值的方法,由风速和风向构成输入特征向量,根据实际风场的风机数据预测风机出力。对该模型进行仿真实验并与实际数据对比,结果表明提出的方法可以有效可靠地进行风机出力预测,具有良好的工程应用前景。

The output of wind turbine has the characteristics of fluctuation and intermittence,which have a big influence on grid-connected wind farm and power grid dispatching.Traditional fan output prediction technology is difficult to achieve accurate prediction results.Based on it,this paper tries to forecast fan output based on practical operating data of actual wind farm using QPSO algorithm to optimize the weights and thresholds of BP neural network,with wind direction and velocity as the input feature vector.The comparison between the simulation test data and actual data proves that the forecast method in this paper is effective and reliable and thus has a promising future in engineering application.