电测与仪表2025，Vol.62Issue(3)：217-224,8.DOI:10.19753/j.issn1001-1390.2025.03.026
量子直流电能表软件可靠性增长优化网络建模
Reliability growth model of quantum direct current electricity meter software based on optimization network
摘要
Abstract
Quantum direct current electricity meter is one of the important instruments in smart grid,the reliabili-ty growth model is of great significance to improve its reliability.In the past,when several types of commonly-used neural networks were used for modeling,there were problems like low parameter training efficiency and low generalization ability caused by unsatisfactory parameters,which reduced the prediction accuracy of the models to a certain extent.In this paper,we will replace the training process of the neural network with a parameter optimi-zation process,and use the improved whole annealing genetic algorithm(WAG A)to optimize the parameters of the back propagation neural network.This improves the modeling efficiency by 18 times and significantly im-proves global optimization ability of the back propagation neural network.Then,the software reliability growth model of WAGA-BPNN is presented,and the experimental data of the software reliability improvement process of quantum DC electricity meter is modeled and verified.Experiments show that the prediction accuracy of the mod-el doubles and meets the practical requirements.关键词
可靠性增长模型/整体退火遗传算法/量子直流电能表Key words
reliability growth model/whole annealing genetic algorithm/quantum direct current electricity meter分类
动力与电气工程引用本文复制引用
田腾,仇茹嘉,赵龙,耿佳琪,王恩惠,孙宇..量子直流电能表软件可靠性增长优化网络建模[J].电测与仪表,2025,62(3):217-224,8.基金项目
国网安徽省电力有限公司科技项目(521205230017) （521205230017）
