基于混沌时间序列的支持向量机短期风速预测模型研究  

Research on short term wind speed prediction model based on chaotic time series using support vector machine

作　　者：黄彦辉[1] 王龙杰[1] 杨薛明[1] 

机构地区：[1]华北电力大学能源动力与机械工程学院,河北保定071003

出　　处：《电测与仪表》2015年第17期32-37,共6页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51301069);河北省自然科学基金资助项目(E2014502042)

摘　　要：风电场风速及风电功率预测技术是加强风电并网管理的关键措施之一。为了提高短期风速预测的精度,减小风电并网对电力系统的电能质量及其安全稳定运行带来的影响,提出了基于混沌时间序列的支持向量机短期风速预测模型。该模型针对风速混沌时间序列建模,并采用基于贝叶斯框架的最小二乘支持向量机对风速进行短期预测。仿真实验结果表明,该预测模型有效地提高了短期风速预测的精度。Wind speed and wind power forecasting technology are key measures to strengthen the grid-connected man- agement of wind power. In order to improve the accuracy of short-term wind forecasting and reduce the impact of wind power grid-connection on power quality and the safe and stable operation of power system, a short term wind speed prediction model based on chaotic time series using support vector machine is proposed. In this model, short-term wind speed prediction is conducted by using least squares support vector machine under the Bayesian framework based on the modeling of chaotic time series of wind speed. Simulation results show that the proposed model can effectively improve the accuracy of short term wind speed prediction.

关 键 词：风电并网管理 短期风速预测 混沌时间序列 最小二乘支持向量机 

分 类 号：TM614[电气工程—电力系统及自动化]