摘要
为探讨如何利用文本处理与聚类算法实现电力调度保供数据的辅助决策,从而提升电力系统运行的可靠性与效率,提出了基于文本特征提取和改进的聚类算法的辅助决策模型。通过数据预处理、TF-IDF文本特征提取以及信息熵优化,结合改进的K均值聚类算法,进行保供数据分类。基于实际调度文本的案例分析表明,提出的模型能够有效提取关键信息,并精准分类不同类型的调度活动,为决策者提供可靠的辅助决策支持。所提方法在提高电力调度决策的科学性和效率方面具有显著优势,能够在电力负荷波动和突发故障情况下为电网运行提供支持。
To explore how text processing and clustering algorithms can be utilized for assisted decision-making in power scheduling sup-ply data to enhance system reliability and efficiency,an assisted decision-making model based on text feature extraction and an improved clustering algorithm is proposed.The method includes data preprocessing,TF-IDF feature extraction,and entropy optimization,and it is integrated with an improved K-Means clustering algorithm to classify supply data.Case studies on actual dispatch texts demonstrate that the proposed model effectively extracts key information and accurately categorizes different types of dispatch activities,providing reliable support for decision-makers.The proposed approach significantly improves the scientific rigor and efficiency of power scheduling deci-sions,supporting grid operations in response to load fluctuations and unexpected faults.
作者
余昕越
孙焜
范展滔
郭岩
高卫东
YU Xinyue;SUN Kun;FAN Zhantao;GUO Yan;GAO Weidong(China Southern Power Grid,Guangzhou Guangdong 510663,China)
出处
《电子器件》
2025年第4期903-908,共6页
Chinese Journal of Electron Devices
基金
中国南方电网电力调度控制中心技改项目(000500SZ23030003)。
关键词
文本处理
聚类算法
保供数据
辅助决策
text processing
clustering algorithms
supply data
assisted decision-making
