摘要
传统的频繁停电管控模式主要是人工多系统查询、手动计算等方式进行统计分析,工作量大,数据分析不全面,严重制约对配电网管理的科学性、先进性和精益化水平。文章将基础数据和频繁停电及停电线变户数据结构化、规范化,形成大数据生态归集和管理,挖掘“数字价值”,使用支持向量机和逻辑回归同时进行预测,减小分类出错的概率,使用随机森林算法加以改进。从客户实际用电体验的角度,研究了新客户电力供应敏感程度的分类模型和计算方法,并采用多种机器学习相结合的方法,基于客户敏感程度以及停电事件相关特征数据来对客户投诉的概率进行预测。
Traditional frequent power outage management mainly uses manual multi-system queries and manual calculations for statistical analysis,resulting in heavy workloads and incomplete data analysis,which severely restricts the scientificity,advancement,and lean level of distribution network management.This paper structures and standardizes fundamental data,frequent power outage data,and line-transformer-customer data related to power outages,forming big data ecological collection and management to mine‘digital value’.It uses both Support Vector Machines(SVM)and Logistic Regression for simultaneous prediction to reduce classification error probability,with improvements made through Random Forest algorithm.From the perspective of customers’actual electricity usage experience,new classification models and calculation methods for customers’power supply sensitivity level are studied.A combined approach using multiple machine learning methods is adopted to predict customer complaint probability based on customer sensitivity and relevant characteristic data of power outage events.
作者
张宏伟
ZHANG Hongwei(Jinchang Electric Power Company,State Grid Gansu Electric Power Company,Gansu Jinchang 737100,China)
出处
《农村电气化》
2025年第3期1-6,47,共7页
Rural Electrification
基金
国家电网有限公司科技项目(W23KJ2704011)。
关键词
配电网
供电台区
大数据
随机森林算法
频繁停电
distribution network
distribution station area
mega data
random forest algorithm
frequent power outages
