A study on reliability of smart meters based on Monte-Carlo method and fault trees

Abstract

Smart meters are widely used in the power supply system, and their operational reliability is closely related to the user's power supply reliability. It is difficult for intelligent power metering equipment to accurately predict its operational reliability and lifespan based on the existing technical specifications. In order to improve the accuracy of predicting the reliability and the maintenance cycle of the smart meter, this paper proposes a method for predicting the reliability of the smart meter based on the Monte Carlo method and fault tree. Firstly, the occurrence time of the bottom sampling event is simulated by the Monte-Carlo method based on the statistical data of the annual failure rate of each module of the smart meter. Then, according to the Fault Tree analysis of smart meters, the occurrence of the event is transformed into the fault time of the whole smart meters. The interval statistics are used to obtain the reliability value of the smart meter. In the end, the curve of the reliability function is obtained after fitting the reliability value. The results show that the reliability of the smart meter obeys the exponential distribution during the operation of 100 years. When it comes to the tenth year, the reliability is 0.9519. This algorithm provides a guide for accurately predicting its reliability and maintenance cycles by modularly analyzing the faults of smart meters.