基于GO法与贝叶斯网络的智能电能表可靠性预计方法研究

随着智能电能表的大面积普及和应用功能的复杂化,智能电能表可靠性的准确预计越来越重要.首先,利用GO图语义性强的特点,将电能表的架构框图转化为GO图;其次,根据GO法的贝叶斯映射法则,将GO图转化为贝叶斯网络,解决GO图操作符众多、算法复杂等问题;然后,对贝叶斯网络各子节点的条件概率表(CPT)进行标注,并将整理好的贝叶斯网络转化为编程语言,利用matlab软件进行可靠性计算;进行贝叶斯双向推理,最终得到电能表的可靠度及各个节点的后验概率分布,对电能表进行故障推断;最后,针对电能表开展加速寿命试验,试验结果验证了方法的科学性与有效性.

Research on reliability prediction method of smart meter based on go method and Bayesian network

with the popularity of smart meters and the complexity of their application functions, it is more and more important to accurately predict the reliability of smart meters. Firstly, using the strong semantic characteristics of go graph, the architecture block diagram of electric energy meter is transformed into go graph; secondly, according to the Bayesian mapping rule of go method, go graph is transformed into Bayesian network to solve the problems of numerous operators and complex algorithm of go graph; and then, the conditional probability table (CPT) of each sub node of Bayesian network is annotated, and the sorted Bayesian network is transformed into coding Finally, the reliability of the meter and the posterior probability distribution of each node are obtained, and the fault of the meter is inferred. The accelerated life test of the meter is carried out, and the test results verify the scientificity and effectiveness of the method.