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Reducing Wildfire Risk through the Use of Advanced Electrical Waveform Monitoring and Analytics


Despite the excellent reliability of electric power distribution systems, lines and apparatus do fail, sometimes catastrophically. Common failure modes often create arcing and/or heating capable of igniting combustible material.

It is well known that powerlines start many brush fires and wildfires which cause substantial property damage and sometimes loss of life. Common ignition sources include arcing downed lines, vegetation contacting lines, sparks ejected from clashing lines, or failing apparatus arcing and/or dropping burning products to 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 analytics that use waveforms from conventional CTs and PTs to detect feeder events, including faults and incipient feeder conditions that, if not addressed, may escalate and start wildfires. The basic concept underlying the application of these waveform analytics, which have become known as distribution fault anticipation (DFA) technology, 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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