用变分自编码器补全电网电缆数据，提升运维预测效果
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders
- 用变分自编码器生成缺失的电缆安装时间等数据
- 在丹麦案例中成功补全资产登记表中的年龄信息
- 适合电网数据不全场景下的智能运维研究者
电力配电电缆网络普遍存在数据不完整、不平衡的问题，影响机器学习模型在预测性维护和可靠性评估中的效果。如电缆安装日期等关键特征常缺失。为应对数据稀缺问题，本研究探讨了变分自编码器（VAEs）在数据增强、合成数据生成、不平衡数据处理及异常值检测中的应用。基于丹麦的原型案例，聚焦于电缆资产登记表中缺失年龄信息的填补，分析表明生成模型在支持数据驱动维护方面具有潜力。但研究也指出需改进特征重要性分析，融入网络结构与外部特征，并处理缺失数据中的偏差。未来应通过半监督学习、先进采样技术，将低电压网络等更多配电网要素纳入VAE分析框架。
原文摘要 · Abstract (English)
Electricity distribution cable networks suffer from incomplete and unbalanced data, hindering the effectiveness of machine learning models for predictive maintenance and reliability evaluation. Features such as the installation date of the cables are frequently missing. To address data scarcity, this study investigates the application of Variational Autoencoders (VAEs) for data enrichment, synthetic data generation, imbalanced data handling, and outlier detection. Based on a proof-of-concept case study for Denmark, targeting the imputation of missing age information in cable network asset registers, the analysis underlines the potential of generative models to support data-driven maintenance. However, the study also highlights several areas for improvement, including enhanced feature importance analysis, incorporating network characteristics and external features, and handling biases in missing data. Future initiatives should expand the application of VAEs by incorporating semi-supervised learning, advanced sampling techniques, and additional distribution grid elements, including low-voltage networks, into the analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。