摘要
随着风力发电技术的发展,风能已成为最具吸引力的可再生能源发电资源之一。然而,由于风能资源具有间歇性和随机性的特点,风力发电系统并入电网将对电力系统的稳定运行和规划带来巨大的冲击和挑战。在此背景下,为了加强电力系统的稳定性和可靠性,提出基于极限学习机的短期风速预测技术,实现精确的风力发电预测。根据开源风速数据的仿真结果表明,该方法不仅能有效地捕捉风速数据的非线性特性,而且与大多数传统方法相比,所需计算时间更短。
With the advancement of wind power generation,the wind energy has become one of the most appealing alternative renewable energy resources.However,due to the intermittent and stochastic nature of wind resource,the wind energy integration also brings great impacts and challenges to the power system operation and planning.To ensure system stability and reliability,accurate wind power forecasting methods become even more important.In this paper,the wind speed forecasting method based on the extreme learning machine (ELM) is investigated.The case study results based on the open resource wind speed data show that the proposed approach not only effectively captures the nonlinear characteristics of wind speed data,but also requires less computation time than most of the conventional approaches.
作者
朱抗
杨洪明
孟科
ZHU Kang;YANG Hong-ming;MENG Ke(School of Electrical and Informathin Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《电力科学与技术学报》
CAS
北大核心
2019年第2期106-111,共6页
Journal of Electric Power Science And Technology
基金
湖南省科技计划项目(2018ZK4045)
关键词
极限学习机
风力发电
风速预测
extreme learning machine
wind power
wind speed forecasting
