A Gaussian Process based Fleet Lifetime Predictor Model for Unmonitored Power Network Assets

Abstract

This paper proposes the use of Gaussian Process Regression to automatically identify relevant predictor variables in a formulation of a remaining useful life model for unmonitored, low value power network assets. Reclosers are used as a proxy for evaluating the efficacy of this method. Distribution network reclosers are typically high-volume assets without on-line monitoring, leading to an insufficient understanding of which factors drive their failures. The ubiquity of reclosers, and their lack of monitoring, prevents the tracking of their individual remaining life, and, confirms their use in validating the proposed process. As an alternative to monitoring, periodic inspection data is used to evaluate asset risk level, which is then used in a predictive model of remaining useful life. Inspection data is often variable in quality with a number of features missing from records. Accordingly, missing inputs are imputed by the proposed process using samples drawn from an advanced form of joint distribution learned from test records and reduced to its conditional form. This work is validated on operational data provided by a regional distribution network operator, but conceptually is applicable to unmonitored fleets of assets of any power network.