李江,王尚玉
LI JIANG,WANG Shangyu

• 1:上海电力大学电气工程学院

摘要(Abstract)：

针对架空线路覆冰灾害频发及传统覆冰预测方法的局限性，提出一种计及气象特征的线路覆冰荷载预测方法。首先，依据线路荷载变化量，将气象环境划分为载荷增长、平衡和衰减3个模式集，并通过灰色关联度分析，将各气象参量的关联权重融入马氏距离算法。其次，为解决气象模式集之间模糊隶属关系的问题，基于历史经验，定义了IGP（覆冰增长势能）因子和IMP（覆冰融化势能）因子，定量描述气象特征变化。最后，将隶属度与监测数据结合，形成含气象特征信息的扩充历史样本，使用SVM（支持向量机）进行覆冰荷载预测。算例对仅考虑气象参量信息、气象过程模糊分类及所提方法3种训练模式的预测结果进行了对比分析，结果表明所提预测模型预测精度更高。
To address the frequent occurrence of ice-induced disasters on overhead transmission lines and the limitations of conventional icing prediction methods, this paper proposes a novel ice load prediction approach that systematically incorporates meteorological characteristics. First, based on the load patterns of transmission lines, meteorological conditions are classified into three distinct regime sets: load growth, equilibrium, and decay. The grey relational analysis is employed to integrate the weighted contributions of meteorological parameters into the Mahalanobis distance. Second, to resolve the fuzzy membership relationships between meteorological regimes, two physically interpretable indices are empirically defined: the ice growth potential(IGP) factor and ice melting potential(IMP) factor, which quantitatively characterize meteorological characteristics. Finally, membership degrees are combined with monitoring data to construct augmented historical samples embedding meteorological characteristics, and a support vector machine(SVM) is applied for ice load prediction. Case studies compare three training paradigms: meteorological parameters only, fuzzy classification of meteorological processes, and the proposed method. Results demonstrate that the proposed model achieves higher prediction accuracy.

关键词(KeyWords)： 输电线路;气象特征;马氏距离;支持向量机
transmission line;meteorological characteristic;Mahalanobis distance;SVM

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(51977030)

作者(Author): 李江,王尚玉
LI JIANG,WANG Shangyu

DOI: 10.19585/j.zjdl.202604001

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