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EPIC 2019: Utilizing Machine Learning Algorithms for Reconstructing Grid-Edge Topologies

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Conclusion • Current State: A current industry practice is to dispatch individual personnel to the field to log and record all assets by visually observing changes and entering the modification into a database. • Problem Statement: With the increased need to observe and manage distribution system, there is great opportunity to develop methodologies for utilities to identify their topology without doing a field verification as field verification is expensive. • Viable Solution: This body of work has utilized machine learning, mutual information, and external data extraction to incorporate utility data to identify an EDC’s grid that is not a simple grid block design but has mountainous terrain with sections of rural and urban areas which include overhead and underground facilities and is highly networked.

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EPIC 2019: Utilizing Machine Learning Algorithms for Reconstructing Grid-Edge Topologies by PITT | SWANSON School of Engineering - Issuu