Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Abstract

This paper presents a surrogate model to quantify the risk of wildfire ignition by individual power lines under extreme weather conditions. Wind speed and wind gust can lead to conductor clashing, which is a cause of igniting disastrous wildfires. The 3D non-linear vibration equations of power lines are employed to generate a dataset that considers physical, structural, and meteorological parameters, including the span of the power line, conductor diameter, wind speed, wind gust, phase clearance, and wind direction. A set of machine learning models is assembled based on these features to generate a score representing the risk of conductor clashing for each power line within a network, quantifying the risk of wildfire ignition. The rendered score represents the chance of the conductor clashing in place of simulating a Runge-Kutta method. A discussion on the impact of various meteorological parameters on power lines under the energization risk is presented. Besides, it is shown how the presented risk measure can be utilized to weigh in the fire safety and service continuity trade-off.

Optimizing Transmission Infrastructure Investments to Support Line De-energization for Mitigating Wildfire Ignition Risk

“…The wildfire ignition risk posed by an energized power line depends on a number of factors involving the environmental conditions around the line and the line's physical characteristics. Translating these factors into numeric risk values is challenging (see, e.g., [59]) and requires detailed data that are not available for our test cases.…”

“…We calculate the risk value r by integrating the WFPI forecast values along each line. This method inherently results in long lines having higher risk values, which is also a characteristic that utilities have correlated with high ignition risk [59].…”

Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization

“…Determining the wildfire ignition risk posed by an energized power line is a challenging task since the risk is dependent on various environmental conditions and the line's physical characteristics [7]. Precise calculations of ignition risk values requires detailed data that are not available for our test case and this is not the focus of this work.…”

“…Typically, utilities de-energize all lines that exceed a predetermined risk threshold, which is based on ambient environmental conditions (e.g., foliage flammability, wind speed, rainfall, and temperature) as well as infrastructure characteristics (e.g., voltage level, and line conditions, lengths and geometries) [7]- [9]. To achieve system operators' goals of making PSPS events "smaller in scope, shorter in duration, and smarter in performance" [10], recent research by Rhodes, Ntaimo, and Roald [9] proposed optimized PSPS events.…”

“…Results from [9] suggest that the optimized PSPS formulation allows system operators to evaluate the tradeoff between wildfire risk and system-wide load shedding, and potentially improve performance relative to a threshold-based approach. Building on this prior work, subsequent research has considered alternate wildfire risk models [7] and infrastructure investment plans to support optimized PSPS events [11].…”