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

作　　者：田腾 仇茹嘉 赵龙 耿佳琪 王恩惠 孙宇 TIAN Teng;QIU Rujia;ZHAO Long;GENG Jiaqi;WANG Enhui;SUN Yu(State Grid Anhui Electric Power Research Institute,Hefei 230000,China;Heilongjiang Electrical Instrumentation Engineering Technology Research Center Co.,Ltd.,Harbin 150028,China)

机构地区：[1]国网安徽省电力有限公司电力科学研究院,合肥230000 [2]黑龙江省电工仪器仪表工程技术研究中心有限公司,哈尔滨150028

出　　处：《电测与仪表》2025年第3期217-224,共8页Electrical Measurement & Instrumentation

基　　金：国网安徽省电力有限公司科技项目(521205230017)。

摘　　要：量子直流电能表是智能电网中的重要仪表之一,其软件的可靠性增长模型对提高其可靠性具有重要意义。以往利用几种常用神经网络进行建模时,存在参数训练效率低,以及参数不理想导致泛化能力低的现象,这在一定程度上降低了模型的预测准确率。为此,文中将神经网络的训练过程转化为参数优化过程,利用完善后的基于退火规则的整体遗传算法(whole annealing genetic algorithm,WAGA)进行BP(back propa-gation)神经网络参数的寻优,使利用BP神经网络建模的效率提高18倍,全局寻优能力明显提高;进而给出了WAGA-BPNN软件可靠性增长模型,并以量子直流电能表的软件可靠性改善过程的实验数据进行建模及预测验证。实验表明,模型的预测准确度提高了1倍,满足实际要求。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(WAGA)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 

分 类 号：TM93[电气工程—电力电子与电力传动]