Generation of In-group Asset Condition Data for Power System Reliability Assessment

TL;DR

This paper presents a novel method for generating realistic in-group asset condition data in power systems, addressing data scarcity issues in reliability assessments by combining models, expert knowledge, and probabilistic diversification.

Contribution

It introduces an unconventional approach to generate asset condition data using degradation, correlation, and categorical models, validated on public datasets.

Findings

Generated data closely matches real asset conditions

Method improves data availability for reliability assessment

Validated with two public datasets

Abstract

In a power system, unlike some critical and standalone assets that are equipped with condition monitoring devices, the conditions of most regular in-group assets are acquired through periodic inspection work. Due to their large quantities, significant amount of manual inspection effort and sometimes data management issues, it is not uncommon to see the asset condition data in a target study area is unavailable or incomplete. Lack of asset condition data undermines the reliability assessment work. To solve this data problem and enhance data availability, this paper explores an unconventional method-generating numerical and non-numerical asset condition data based on condition degradation, condition correlation and categorical distribution models. Empirical knowledge from human experts can also be incorporated in the modeling process. Also, a probabilistic diversification step can be taken to make the generated numerical condition data probabilistic. This method can generate close-to-real asset condition data and has been validated systematically based on two public datasets. An area reliability assessment example based on cables is given to demonstrate the usefulness of this method and its generated data. This method can also be used to conveniently generate hypothetical asset condition data for research purposes.