郑文哲,王振国,石远钊,侯慧
ZHENG Wenzhe,WANG Zhenguo,SHI Yuanzhao,HOU Hui

• 1:国网浙江省电力有限公司电力科学研究院
• 2:武汉理工大学新能源与电气工程学院

摘要(Abstract)：

极端冰灾频发严重威胁电网安全运行，致使应急资源调配面临严峻挑战。为精准预测冰灾下的应急资源需求，提出一种基于WGAN-Stacking(Wasserstein生成对抗网络和Stacking集成学习)的电网覆冰灾害应急资源需求预测方法。首先，整合气象、地理及电网多源数据，利用WGAN生成故障样本，并通过Fréchet Inception距离与核Inception距离评估生成样本质量，以缓解样本不平衡问题。其次，构建Stacking集成学习框架，以随机森林、梯度提升决策树与支持向量机作为基学习器，以前馈神经网络作为元学习器，融合多模型优势以提升故障预测稳定性与精准性。然后，基于预测故障的点位，结合历史抢修单元配置规律，实现抢修人员与抢修车辆分区、分类的应急资源需求预测。最后，以2024年浙江省冰灾数据进行算例分析，结果表明，所提方法能够有效预测冰灾下的应急资源需求，为应急资源配置提供指导。
The frequent occurrence of extreme icing disasters poses a severe threat to the secure operation of power grids, presenting significant challenges for the deployment of emergency resources. To accurately forecast emergency resource demands during icing disasters this paper proposes an emergency resource demand forecasting method for power grid under icing disasters based on WGAN-Stacking(Wasserstein generative adversarial networks and Stacking ensemble learning). First, multi-source meteorological, geographical, and grid data are integrated. A WGAN is utilized to generate fault samples and the quality of these generated samples is evaluated using the Fréchet Inception Distance(FID) and Kernel Inception Distance(KID) to alleviate data imbalance issues. Second, a Stacking ensemble learning framework is constructed, employing random forest, gradient boosting decision trees, and support vector machines as base learners, with a feed-forward neural network acting as the meta-learner; this framework leverages the complementary strengths of multiple models to enhance robustness and accuracy of fault forecasting. Based on the forecasted fault locations and historical patterns of emergency repair unit allocation region-specific and category-specific forecasting of emergency resource demands—specifically for repair personnel and vehicles—is achieved. Finally, a case study is conducted using icing disaster data from Zhejiang Province in 2024. The results demonstrate that the proposed method effectively forecasts emergency resource demands under icing disaster conditions, providing practical guidance for emergency resource allocation.

关键词(KeyWords)： 应急资源;WGAN;样本不平衡;Stacking;冰灾
emergency resource;WGAN;imbalanced sample;Stacking;icing disaster

Abstract:

Keywords:

基金项目(Foundation): 浙江省自然科学基金（LZJMY25D050006）;; 国网浙江省电力有限公司科技项目（B311DS25Z009）

作者(Author): 郑文哲,王振国,石远钊,侯慧
ZHENG Wenzhe,WANG Zhenguo,SHI Yuanzhao,HOU Hui

DOI: 10.19585/j.zjdl.202610004

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