ASSESSMENT OF REMAINING LIFETIME AND FAILURE PROBABILITY FOR NETWORK COMPONENTS – A PRACTICAL APPROACH

The paper describes a methodology for estimating annual failure probabilities and remaining lifetime of network components, based on a simplified model of a component’s deterioration curve, expert judgments on typical lifetimes and knowledge about its technical condition. In order to evaluate the method a case study based on deterioration mechanisms on circuit breaker sub-components (> 132 kV) has been performed. The case study shows that the methodology is an adequate alternative to methods based on more statistical analyses on fault data, due to the fact that such data very often is scarce. INTRODUCTION The power network infrastructure consists of a large number of components. In order to optimize maintenance and renewal, the network companies need better models and tools based on knowledge about expected residual lifetime and failure probability for critical components. The benefits of such models are most evident regarding the network asset management, and primarily in the maintenance and renewal planning. This type of decisions is typically of the kind “should this component be renewed now or is it profitable to wait e.g. 5 years? ”, or: “How often should we perform condition monitoring (inspection) on this component? ” This is a situation where the decision maker has to consider the technical capacity limits of the component as well as the probability of failure. A failure may result both in economical and non-economical consequences. The problem is, however, that we neither know for sure if or when a failure will occur, nor what the exact consequences will be. Probabilistic life models are a valuable support for the decision maker in such evaluations. Various models were proposed over the past decades. Anders, Endrenyi and Leite da Silva presented in [1] maintenance optimization models based on a Markov chain and computer software for maintenance management. An often applied method to model deterioration is the use of the gamma process. A short overview about work on this topic is given by Kallen and Noortwijk in [2]. METHODOLOGY Consider the general life curve (deterioration curve) for a component shown in Figure 1. The interpretation of the curve is as follows: The component may remain “as good as new” for many years, but as time goes, both constant and stochastic strain will degrade the component, which will (slowly) lose its original strength. At one point the component will be exposed to larger stress than its remaining strength, and a failure occurs. It is important to bear in mind that a curve as the one shown in Figure 1 is applicable only for a single sub-component, and for a single failure/degradation mechanism. Figure 1 General life curve If the exact life curve is difficult (or expensive) to reveal for a certain component, a simplification may be appropriate. The technical condition of a component can be characterized on a scale from 1 to 5 according to handbooks for condition monitoring of hydro power components, published by the Norwegian Electricity Industry Association (EBL Kompetanse) [3]. Here, the continuous degradation of a component is simplified by dividing its life into four states. The state description is given in Table 1 and these four states are denoted main states k. A component asgood-as-new is in state k = 1. When the condition is characterized as critical, the state is k = 4 and normally maintenance actions must be taken immediately in order to avoid state 5 (failure). Time [yr] Residual strength [%] Length of life Normal stress Stochastic stress Critical level