Optimal wind power prediction based on local ramp error correction of wind speed

XIAO Yi; LI Chenghuang; LIU Ruoping; ZUO Jian; LI Yinhong

doi:10.16081/j.issn.1006-6047.2019.03.029

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Optimal wind power prediction based on local ramp error correction of wind speed

更新时间：2025-04-25

- Optimal wind power prediction based on local ramp error correction of wind speed
- Vol. 39, Issue 3, Pages: 182-188(2019)
- 作者机构：
  
  1. 华中科技大学电气与电子工程学院强电磁工程与新技术国家重点实验室
  2. 华中科技大学电气与电子工程学院电力安全与高效湖北省重点实验室
  3. 长江勘测规划设计研究有限责任公司
  4. 广东电网有限责任公司电力调度控制中心
- 作者简介：
- 基金信息：
- DOI：10.16081/j.issn.1006-6047.2019.03.029
  CLC： TM614
- Published：2019
- 稿件说明：
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[1]肖逸,李程煌,刘若平,左剑,李银红.基于风速局部爬坡误差校正的风电功率优化预测[J].电力自动化设备,2019,39(03):182-188.

XIAO Yi, LI Chenghuang, LIU Ruoping, et al. Optimal wind power prediction based on local ramp error correction of wind speed[J]. 2019, 39(3): 182-188.
[1]肖逸,李程煌,刘若平,左剑,李银红.基于风速局部爬坡误差校正的风电功率优化预测[J].电力自动化设备,2019,39(03):182-188. DOI： 10.16081/j.issn.1006-6047.2019.03.029.

XIAO Yi, LI Chenghuang, LIU Ruoping, et al. Optimal wind power prediction based on local ramp error correction of wind speed[J]. 2019, 39(3): 182-188. DOI： 10.16081/j.issn.1006-6047.2019.03.029.

摘要

准确的风电功率预测对于电力系统安全稳定运行具有重要意义

滞后性是产生风电功率预测误差的主要原因

尤其是风速变化较快时

滞后性引起的预测误差较大。考虑到风速波动与风电功率的变化息息相关

提出一种基于风速局部爬坡（LR）误差校正的方法来改善预测风速的滞后性

并将校正后的预测风速及历史功率数据作为输入进行风电功率预测。提出利用灰狼优化（GWO）算法对最小二乘支持向量机（LSSVM）的参数进行优化

以提高风电功率预测的准确性。算例结果表明

所提方法能够有效提高风电功率预测精度。

Abstract

Accurate wind power prediction is significant for secure and stable operation of power system

and the lag is the main reason of wind power prediction error

especially when wind speed changes rapidly

the lag will result in big error. Considering strong relationship between wind speed and wind power

an error correction method based on LR（Local Ramp） of wind speed is proposed to improve the lag

and the predicted wind speed after correction and historical wind power are taken as input for wind power prediction. The parameters of LSSVM（Least Square Support Vector Machine） are optimized by using GWO（Grey Wolf Optimization） algorithm to improve the accuracy of wind power prediction. Case results show that the proposed method can effectively improve the accuracy of wind power prediction.

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