Substation Signal Matching with a Bagged Token Classifier
This addresses a specific efficiency problem for engineers at substation service providers, but it is incremental as it builds on existing classification methods.
The paper tackles the manual matching of customer data to internal signal names in substations by proposing a bagged token classifier that automates the process, achieving better accuracy and efficiency compared to standard classifiers.
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.