基于多源数据的工业用户功率因数治理决策模型研究  

Industrial User Power Factor Governance Decision Model Based on Multi-source Data

作　　者：李语菲 LI Yu-fei(Xiamen University of Technology,Xiamen 361021,China)

机构地区：[1]厦门理工学院,福建厦门361021

出　　处：《电气开关》2024年第4期23-28,31,共7页Electric Switchgear

摘　　要：首先根据工业用户力率考核费用及实际力率,创新提出无功治理降损潜力用户标签模型,对用户分层级贴上六类标签;接着,结合配网变压器、线路等参数,创新提出无功治理效益分析模型,挖掘力率不达标引起的用户损失及电网侧供电电量损失;再次基于支持向量机和主动学习,创新提出用户无功治理状态及需求跟踪关联模型,辨识用户无功治理状态,以便进一步制定服务策略。最后,在某供电公司所辖区域用户验证了模型的经济降损效果。This article first innovatively proposes a user label model for reactive power governance and loss reduction potential based on the assessment cost of power rate and actual power rate,and labels six categories of users at different levels;Next,based on parameters such as distribution network transformers and lines,an innovative analysis model for reactive power management benefits is proposed to explore the user losses and grid side power supply energy losses caused by substandard power rates;Thirdly,based on support vector machine and active learning,an innovative correlation model of user reactive power governance status and demand tracking is proposed to identify user reactive power governance status,so as to further develop service strategies.Finally,the economic loss reduction effect of the model was verified by users in the area under the jurisdiction of a certain power supply company.

关 键 词：融合数据 无功画像 支持向量机 主动学习法 技术降损 

分 类 号：TM73[电气工程—电力系统及自动化]