Reliability and Component Importance Analysis of Substation Automation System in Multi-State Mode

A substation automation system (SAS) is usually considered binary perspective, but majority of the SASs degrade before complete failure, they should be considered as a multi-state system (MSS). Therefore, this paper proposes a new reliability analysis model for SAS in multi-state mode. Unlike conventional binary-state reliability methods, the proposed method takes into account the various states of components, which can more accurately compute the probabilities of SASs in any states, and further meet SAS Delicacy Management, such as wiring type selection, maintenance time decision and so on. In this paper, SAS is viewed as a network consisting of functional related automation devices (AD), which can be described by The Structural Functions and The Correlation Matrices. We assess the dynamic element state probabilities of each aging multi-state AD using a stochastic process model, which is identified as a Markov model. The state distribution of the entire SAS is evaluated via the combination of the stochastic process and universal generating function. Finally, it takes four typical SAS as an example to describe the validity and superiority of the multi-state model in the reliability evaluation. This paper also proposes an importance analysis model of MSS components to analyze the importance degree of ADs in SAS.