| 引用本文: | 饶逸洲,刘兴杰,梁英,等.基于 mRMR-GSWOA-XGBoost 的输电线路覆冰等级预测方法[J].电力系统保护与控制,2026,54(10):93-102. |
| RAO Yizhou,LIU Xingjie,LIANG Ying,et al.Ice accretion level prediction method for transmission lines based on mRMR-GSWOA-XGBoost[J].Power System Protection and Control,2026,54(10):93-102 |
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| 摘要: |
| 针对偏远地区未部署在线监测系统、仅依赖邻近气象站数据导致覆冰预测精度不足的问题，从覆冰增长具有累积特性出发，结合覆冰预测数据高维度、强非线性等特点，提出一种基于 mRMR-GSWOA-XGBoost 的输电线路覆冰等级预测方法。首先，构建融合气象数据、地理数据、线路特征和覆冰厚度的多源数据特征集，通过归一化和标签化处理，实现多源信息有机耦合。利用最小冗余最大相关性 (minimum redundancy maximum relevance, mRMR) 筛选能够显著表征覆冰影响因子的特征，以提升模型的泛化能力和抗过拟合能力。然后，通过改进鲸鱼优化算法 (global search whale optimization algorithm, GSWOA) 对极限梯度提升树 (extreme gradient boosting, XGBoost) 模型超参数进行优化，增强模型强非线性建模能力。最终建立多分类模型，实现未来 12 h 覆冰等级预测。结果表明，该方法识别覆冰等级的平均准确率和最大准确率分别达到 90.7% 和 90.47%，验证了模型在覆冰等级预测中的准确性和可靠性，为未部署在线监测系统的输电线路覆冰预测提供参考与建模思路。 |
| 关键词: 输电线路 覆冰预测 XGBoost 模型 覆冰等级 最小冗余最大相关性 |
| DOI：10.19783/j.cnki.pspc.251293 |
| 分类号: |
| 基金项目:国家自然科学基金地区基金项目资助 (12062023)；2021 年自治区重点研发计划社发领域项目资助 (2021BEG03029)；国网宁夏电力有限公司科技项目资助 (5229JY240009) |
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| Ice accretion level prediction method for transmission lines based on mRMR-GSWOA-XGBoost |
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RAO Yizhou1, LIU Xingjie1, LIANG Ying1, BO Tianli1, ZHAO Tao2
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1. College of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China; 2. Department of Electric Power Engineering, North China Electric Power University, Baoding 071003, China
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| Abstract: |
| To address the problem of insufficient icing prediction accuracy in remote areas where no online monitoring systems are deployed and only nearby meteorological station data are available, this paper proposes a transmission line icing level prediction method based on mRMR-GSWOA-XGBoost. Considering the cumulative nature of ice accretion growth, along with the high dimensionality and strong nonlinearity of icing prediction data, a multi-source data feature set is first constructed by integrating meteorological data, geographical data, line characteristics, and icing thickness. Through normalization and labeling, heterogeneous information is effectively coupled. The minimum redundancy maximum relevance (mRMR) method is then used to select features that significantly represent the influencing factors of icing, thereby improving the model's generalization ability and resistance to overfitting. Subsequently, an improved global search whale optimization algorithm (GSWOA) is applied to optimize the hyperparameters of the extreme gradient boosting (XGBoost) model, enhancing its capability to capture strong nonlinear relationships. Finally, a multi-classification model is established to predict icing levels over the next 12 hours. Results show that the proposed method achieves an average accuracy of 90.7% and a maximum accuracy of 90.47% in identifying icing levels, demonstrating its effectiveness and reliability. This study provides a useful reference and modeling approach for transmission line icing prediction without online monitoring systems. |
| Key words: transmission line icing accretion prediction XGBoost model icing grade minimum redundancy maximum relevance |
