湖南电力 ›› 2025, Vol. 45 ›› Issue (5): 80-84.doi: 10.3969/j.issn.1008-0198.2025.05.011

• 电网运行与控制 • 上一篇    下一篇

面向省级区域的风电覆冰退备容量预测方法

戴文1,2, 李波1,2, 谢林瑾1,2, 康文军1,2, 杨博远1,2, 怀晓伟1,2   

  1. 1.湖南防灾科技有限公司, 湖南 长沙 410129;
    2.电网防灾减灾全国重点实验室, 湖南 长沙 410129
  • 收稿日期:2025-06-09 修回日期:2025-08-03 发布日期:2025-11-11
  • 通信作者: 怀晓伟(1991),男,汉族,理学博士,高级工程师,从事电网及新能源领域灾害预测预警研究工作。
  • 作者简介:戴文(1995),女,满族,工学硕士,助理工程师,研究方向为电力防灾减灾与人工智能技术。
  • 基金资助:
    国网湖南省电力有限公司科技项目(5216AF24000M)

Study on Provincial Regional Prediction Methods for Wind Turbine Capacity Loss Caused by Icing

DAI Wen1,2, LI Bo1,2, XIE Linjin1,2, KANG Wenjun1,2, YANG Boyuan1,2, HUAI Xiaowei1,2   

  1. 1. Hunan Disaster Prevention Technology Co., Ltd., Changsha 410129, China;
    2. State Key Laboratory of Disaster Prevention and Reduction for Power Grid, Changsha 410129, China
  • Received:2025-06-09 Revised:2025-08-03 Published:2025-11-11

摘要: 冬季寒潮过程中,某省高寒山地风电场风机易出现覆冰退备现象,给电网调度和电力平衡带来挑战。针对此问题,提出一种面向区域的风电覆冰退备容量预测方法,该方法同时考虑雨凇、雾凇天气,引入冰冻气象指数(IFM),量化描述寒潮过程中风电覆冰的外部气象条件,建立模型将IFM映射至风电场覆冰退备容量,结合多轮典型寒潮过程开展实证检验。结果表明,所提方法在多个寒潮过程中均展现出良好的预测精度,在2024—2025年冬季的覆冰退备容量预测中,平均准确率达到85.44%,具有良好的实际应用价值和区域适用性。

关键词: 风电覆冰, 退备容量预测, 冰冻气象指数

Abstract: During winter cold wave events, wind turbines in alpine mountainous wind farms in a certain province are prone to capacity loss caused by icing, posing significant challenges to grid dispatching and power balance. To address this issue, a regional prediction method for wind turbine capacity loss caused by icing is proposed, which takes into account both meteorological conditions of glaze ice and freezing fog, and introduces a Freezing Meteorological Index(IFM) to quantitatively describe the external meteorological conditions of wind power ice-covered in the process of winter cold waves. A predictive model is constructed to map IFM to wind power capacity loss due to icing. Empirical validation is conducted using multiple typical cold wave events. Results show that the proposed method demonstrates high prediction accuracy across several cold wave processes, and the average accuracy reaches 85.44% in the prediction of capacity loss due to icing in the winter of 2024-2025, indicating strong practical value and regional applicability.

Key words: wind turbine icing, prediction of capacity loss, freezing meteorological index

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