Optimizing Transformer Management: GIS Analysis and ESRI Tools Field Deployment

Abstract

The COVID-19 pandemic disrupted supply chains, causing transformer costs to rise two to five times when compared to the 2019 cost, and extending lead times from a few months to over eighteen months. To address this, the Navajo Tribal Utility Authority (NTUA) aimed to locate idle transformers, referred to as "hanging inventory," for reuse. However, NTUA lacked an efficient system for tracking these transformers and relied on employee reports from field visits. To improve this process, I developed a Python-based GIS analysis using existing transformers, powerline, and meter point data to map the relationships between transformers and downline meters. By cross-referencing active meter locations, I identified over 2,000 potential idle transformers. We deployed over 100 tablets equipped with ESRI field software to assist field workers in accurately capturing and updating transformer locations. The project enabled significant data cleanup, uncovering mislocated meters and errors between the automated metering infrastructure system and the GIS database. These findings have enhanced NTUA's asset management by identifying unused transformers, improving resource allocation, and streamlining future maintenance efforts. As a result, NTUA can now effectively manage transformers, helping to mitigate ongoing supply chain challenges.