Substation Signal Matching with a Bagged Token Classifier

TL;DR

This paper introduces a bagged token classifier that automates the matching of customer signal names to provider signal names in substations, improving accuracy and efficiency over traditional methods.

Contribution

The paper presents a novel bagged token classifier approach for automating substation signal name matching, outperforming standard classifiers in accuracy and efficiency.

Findings

Higher accuracy than standard classifiers

Improved efficiency in signal matching

Effective use of token voting in classification

Abstract

Currently, engineers at substation service providers match customer data with the corresponding internally used signal names manually. This paper proposes a machine learning method to automate this process based on substation signal mapping data from a repository of executed projects. To this end, a bagged token classifier is proposed, letting words (tokens) in the customer signal name vote for provider signal names. In our evaluation, the proposed method exhibits better performance in terms of both accuracy and efficiency over standard classifiers.