Modified HDW Wildfire Forecasting for Utility Component Shutoffs
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Solution Overview
Problem
Utilities face a dilemma in wildfire-prone areas where disabling components to prevent outages that could cause wildfires disrupts service, and existing forecasting methods lack the granularity to accurately assess and mitigate wildfire risks on a per-component basis.
Innovation Solution
A machine learning-based wildfire forecast tool integrates outage risk predictions, ignition probabilities, and wildfire impact simulations to provide a granular catastrophic wildfire risk score for utility components, enabling informed shutoff decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If utility components are disabled to prevent outages that could cause wildfires, then wildfire risk is reduced, but service disruption increases
Solution Approach 1:
The system segments the utility network into individual components and assesses wildfire risk for each component separately using per-component risk scores. This allows utilities to selectively shut off only high-risk components rather than disabling entire networks, thereby maintaining service availability for low-risk components while still mitigating wildfire risk.
Solution Approach 2:
The system applies local quality by providing granular, location-specific risk assessments for different utility components based on their unique characteristics and environmental conditions. Each component receives a customized risk score considering factors like vegetation proximity, weather conditions, and component-specific vulnerability, enabling targeted risk management rather than blanket shutdowns.
2Measurement precision
If existing forecasting methods are used, then general wildfire risk can be assessed, but per-component risk granularity is insufficient
Solution Approach 1:
The system adds a new dimension of granularity by transitioning from aggregate wildfire risk forecasting to per-component risk assessment. It incorporates component-specific attributes (age, material, location) alongside environmental factors, creating a multi-dimensional risk evaluation framework that delivers precise, component-level predictions without requiring complete system redesign.
Solution Approach 2:
The system achieves universality by developing a multi-functional forecasting platform that can assess various types of utility components (power lines, transformers, switches) using a unified risk assessment methodology. The same core model adapts to different component types and geographic regions, providing consistent per-component risk scores across diverse utility infrastructures.
Data Source
AI summary
A service determines a Hot Dry Windy (HDW) index for a vicinity around a utility component by inputting atmospheric conditions and weather conditions in the vicinity into a HDW model and receiving the HDW index as output from the HDW model. The service determines an Energy Release Component (ERC) percentile by inputting fuel loading and combustibility characteristics into an ERC model and receiving, as output from the ERC model, the ERC percentile. The service aggregates the HDW index and the ERC percentile into a modified HDW (mHDW) metric, inputs forecasted fire characteristics for the vicinity and the HDW index into a machine learning model, and receives as output from the machine learning model a likelihood of a fire growing to a threshold size. The service displays fire risk metric for the vicinity based on the likelihood of the fire growing to the threshold size.