Estimating Asset Class Health Indices in Power Systems

TL;DR

This paper introduces a sequence learning approach to estimate asset class health indices in power systems, addressing a gap in preventative asset management by predicting future asset health based on shared characteristics.

Contribution

It presents a novel data-driven sequence learning method for estimating health indices of power asset classes, improving prediction accuracy over existing methods.

Findings

Superior performance compared to other estimation methods

Effective for preventative asset management

Validated on real utility data

Abstract

Power systems have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current or near-future asset health index based on the current condition data. For preventative asset management, it is highly desirable to estimate asset health indices, especially for asset classes in which the assets share similar electrical and/or mechanical characteristics. This important problem has not been sufficiently addressed. This paper proposes a sequence learning based method to estimate health indices for power asset classes. A comprehensive data-driven method based on sequence learning is presented and solid tests are conducted based on real utility data. The proposed method revealed superior performance with comparison to other Estimation methods.