基于WRF模式和PSO-LSSVM的风电场短期风速订正  

Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method

作　　者：叶小岭[1,2] 顾荣 邓华[1,2] 陈浩[1] 杨星 

机构地区：[1]南京信息工程大学信息与控制学院,江苏南京210044 [2]南京信息工程大学气象灾害预报预警与评估协同创新中心,江苏南京210044

出　　处：《电力系统保护与控制》2017年第22期48-54,共7页Power System Protection and Control

基　　金：国家自然科学基金项目(41675156);国家公益性行业(气象)科研专项(GYHY20110604);江苏省六大人才高峰项目(WLW-021)资助;江苏省研究生创新工程省立项目(SJZZ16_0155)~~

摘　　要：风速预测是风电场风电功率预测的基础与前提,以数值天气预报(WRF模式)为基础进行风速预测,为了提高WRF模式预测的准确性,采用最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)对WRF模式输出的风速进行订正。同时,为提高LSSVM算法的精确度和减小拟合过程的复杂度,采用粒子群优化算法(Particle Swarm Optimization,PSO)对其参数进行优化。试验结果表明:采用LSSVM订正可以进一步减小WRF模式预测风速的误差,再经过PSO优化后,相对均方根误差和相对平均绝对误差降低了5%~10%,均方根误差下降了0.5 m/s。与未经优化的LSSVM以及极限学习机(ELM)算法对比分析后得出,粒子群优化最小二乘支持向量机(PSO-LSSVM)对WRF模式预测的风速有较好的订正效果,能进一步提高风速预测的准确性。Wind speed forecasting is the base and precondition of wind power prediction of wind farm. The Numerical Weather Prediction （WRF） model is used to predict wind speed. In order to improve the accuracy of WRF model, the Least Square Support Vector Machine （LSSVM） is used to correct the wind speed of the output of the WRF model. At the same time, in order to improve the accuracy of the LSSVM model and reduce the complexity of the fitting process, Particle Swarm Algorithm （PSO） is used to optimize the parameters. Experimental results show that the LSSVM can further reduce the error of WRF model in predicting wind speed sequence, and the relative root mean square error and the relative to the average absolute error are reduced by 5%-~10%, the RMS error decreased by 0.5 m/s. Compared with without optimized LSSVM and ELM, PSO-LSSVM has a better correction effect in wind speed predicting by WRF to improve the accuracy of wind speed forecasting.

关 键 词：风力发电 风速订正 WRF模式 PSO-LSSVM 预测效果 

分 类 号：TM614[电气工程—电力系统及自动化]