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Application of Advanced Electrical Waveform Monitoring and Analytics for Reduction of Wildfire Risk

Abstract

Modern power distribution systems enjoy excellent reliability, but lines and apparatus do fail, sometimes catastrophically. Failing apparatus often create arcing and/or heating capable of igniting proximate combustible material.

Power lines are known to cause many brush fires and wildfires, which result in substantial property damage and sometimes loss of life. Common ignition sources include arcing downed conductors, vegetation contacting conductors, sparks ejected from clashing conductors, or failing apparatus arcing and/or dropping burning products on the ground. To the extent that such failures can be detected and repaired quickly, many wildfires can be prevented or minimized.

Smart-grid efforts typically focus on hastening restoration following an outage. These systems provide value, but they do not detect incipient failures and temporary faults that not only cause customer interruptions and outages, but also more serious damage including wildfires. Texas A&M University has developed sophisticated waveform analytics to detect feeder events, including faults and incipient feeder conditions that, if not addressed,  may escalate to ignite wildfires. The basic concept underlying the application of these waveform analytics is described. Case studies provide concrete examples of the ability to detect, locate, and repair failing devices before they create ignition sources capable  of causing wildfires.

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