微型电脑应用2025，Vol.41Issue(3)：82-85,89,5.
面向电力工程数据智能校核的小样本模型训练技术
Small Sample Model Training Technology for Intelligent Verification of Power Engineering Data
摘要
Abstract
To solve the problem of low accuracy of power engineering data verification,this paper proposes an intelligent verifi-cation model for small sample data of power engineering.The model combines two-stage fuzzy C-means clustering(TSFCM)with multi-population particle swarm optimization support vector machine(MPSO-SVM).This model uses the TSFCM algo-rithm to classify the input historical data of power engineering to reduce the impact of differences in the characteristics of differ-ent types of power engineering features on data verification results.The MPSO-SVM algorithm is used to construct a verifica-tion model for each type of power engineering data to achieve accurate verification of power engineering data.The simulation analysis results using cost data as samples show that the proposed TSFCM algorithm has better clustering accuracy and compu-tational speed,and the proposed MPSO-SVM algorithm has smaller verification error.Compared with traditional SVM algo-rithm,the average verification error decreases from 9.3％to 4.7％.关键词
电力工程数据/多种群粒子群/支持向量机/数据校核/C均值聚类Key words
power engineering data/multi-population particle swarm/support vector machine/data verification/C-means clus-tering分类
信息技术与安全科学引用本文复制引用
田海丰,华生萍,杨蒲寒婷,才海多杰,刘舒宁..面向电力工程数据智能校核的小样本模型训练技术[J].微型电脑应用,2025,41(3):82-85,89,5.基金项目
国网青海省电力公司管理研究类项目(Q2021RCDT2B0713) （Q2021RCDT2B0713）
