Health Management of Dry-Type Transformer Based on Broad Learning System

Abstract

This article presents a novel health management method of the dry-type transformer to diagnose the early unhealthy behavior and evaluate the transformer's health condition by health score. The health condition diagnosis implemented by a proposed dynamic-weighted-feed-back broad learning system (BLS) (DW-FB-BLS) method, which helps to determine the BLS network structure effectively, and adjusts the weight of features in the online application to avoid reduction of accuracy caused by concept drift. Then, a rational score rule is set to evaluate the health condition of the dry-type transformer by health score, which allows intuitive presentation and preservation of transformer's health condition over a long period. Finally, the effectiveness and validity of the proposed method are verified based on the real field data of dry-type transformer. Satisfactory results for unhealthy behavior diagnosis and health evaluation are obtained, it shows that health management of this article can reflect the real health condition of dry-type transformer appropriately.