Early Wildfire Detection with Line Sensors

<p>Over the last few years, wildfires have become more devastating to communities as the fires are inevitably destructive to many homes, businesses, and ecosystems. Frequent wildfires also pose a significant threat to power grids and nearby residents as they can damage transmission lines and other electrical equipment, which in turn can cause major power shutdowns. Especially in western U.S., severe drought conditions and weather variability cause residents to become more vulnerable to wildfire disasters as their safety is threatened. We are incompetent to control the wildfires effectively despite existing advanced technologies. Hence, an algorithm based on energy conservation and heat transfer mechanisms is created to examine the feasibility of line sag sensors to detect wildfires in an early stage. To test the algorithm, it is integrated with a 150-bus synthetic power network using MATLAB. The resulted conductor temperature from randomly selected parameters like fire locations, weather conditions, and fire rate of spread causes the change in line sag over 10 minutes. The line sag behavior is then analyzed under different scenarios. By monitoring real-time power line sag measurements, the analysis shows that early onset wildfires can be detected in less than 3 minutes and up to about 1 km from the power line to the fire. It is also suggested the utilization of silica fabrics on the sensors can provide thermal and fire protection while having no impact to the power line magnetic fields.</p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2021

Author dc:creator

• Yan, Virginia

Contributors dc:contributor

• Majid Poshtan
• Electrical Engineering
• College of Engineering

• Wildfire
• Detection
• Line Sensors
• Sag
• Heat-Transfer
• MATLAB
• Electrical and Computer Engineering
• Electrical and Electronics
• Forest Sciences
• Power and Energy

Identifier

10.15368/theses.2021.5

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3784