Multistate time lag dynamic Bayesian networks model for reliability prediction of smart meters

Abstract

Smart meters are the terminals of smart grids and have the characteristics of complex systems that units in the systems always have many tasks. The reliability prediction model of smart meters is less investigated and traditional models with the two-condition hypothesis are not applicable. In complex systems, multitasking unit has degradation characteristic. Failures of different tasks will affect other units with varying degrees and even lead to the failures of other units in advance. Hence, linear correlation analysis is unsuitable for failure correlation with time lags. Firstly, this paper proposes a two-parameter correlation estimated method for multistate units and multitasking units. The status data of units are divided into one-dimensional and two-state time series, cross-correlation coefficients and time migrations are obtained by analysing two-state time series and used to describe the time-lag correlation. Secondly, the Conditional Probability Table (CPT) of the Dynamic Bayesian Network (DBN) is updated by adding cross-correlation coefficients and time migrations, then time-lag Dynamic Bayesian Network (TDBN) is built. Additionally, the simulation analysis shows that prediction results of TDBN are more consistent of mean failure data. Lastly, the TDBN of the smart meter is built, and the effectiveness of TDBN is further proved by analysing the failure data of smart meters.