关键词: 风电功率预测, 粒子群优化, 核极限学习机, 径向基函数神经网络

Abstract:

Randomness of wind speed and wind direction leads to large fluctuations in wind fleet capacity.Accurate forecasting of wind energy can provide an important basis for improving the power grid performance,enhancing the power grid's ability to receive wind power and timely arranging the maintenance plan of wind farm.This document provides the particle Legion optimization kernel restricted learning machine (PSO-KELM) algorithm.Preprocess SCADA data of wind turbine generator,supplement and correct abnormal data;Aiming at the multicollinearity problem of extreme learning machine,A foreign learning machine is proposed on the basis of nuclear operation to prevent accidents from driving out a foreign learning machine.The optimization algorithm of particle emissions (PSO) is used to optimize the penile factor and the radiation parameters of the KELM nucleus and generate an external learning model based on optimization of particle emissions.Go on.Compared to other ways like BPNN and RBFNN,this model is confirmed by real wind farm data.

Key words: wind power prediction, particle swarm optimization, nuclear limit learning machine, radial basis function neural network