Probabilistic Storm Outage Prediction for Utility Infrastructure
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Solution Overview
Problem
Conventional outage management systems are reactive and ineffective in predicting and preparing for utility infrastructure damage during severe weather events, leading to prolonged power outages and inefficient resource deployment.
Innovation Solution
A storm outage management system (SOMS) that uses meteorological data, including weather forecasts and sensor-collected information, to predict potential damage to utility infrastructure, allowing for proactive resource deployment and quick restoration of power services by determining the location, timing, and number of potential outages based on probabilistic ice and wet-snow accretion predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional reactive outage management systems are used, then resource deployment responds to actual outages, but power restoration time is prolonged due to lack of predictive capability
Solution Approach 1:
The system performs preliminary actions by predicting outage locations and timing before storms occur, enabling utilities to pre-position crews and resources in advance. The predictive modeling analyzes weather forecasts, infrastructure data, and historical outage patterns to identify high-risk areas, allowing proactive deployment rather than reactive response.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual outage occurrences against predictions, validating and refining the predictive models. Historical outage data is fed back into the system to improve future prediction accuracy, creating a learning loop that enhances reliability over time.
2Productivity
If conventional reactive systems dispatch crews based on customer reports, then resource deployment matches actual damage locations, but resource deployment efficiency decreases due to lack of advance positioning
Solution Approach 1:
Crews and resources are preliminarily positioned in predicted high-risk areas before storms arrive. The system generates predictive outage maps that guide pre-storm crew staging, enabling rapid response when outages actually occur without requiring complex real-time redistribution logistics.
Solution Approach 2:
The utility service territory is segmented into zones based on predicted outage risk, allowing differentiated resource allocation. High-risk zones receive prioritized crew positioning and resource allocation, while lower-risk areas receive standard deployment, optimizing overall efficiency without uniform complexity.
3Loss of time
If proactive predictive modeling is implemented, then power restoration time is reduced through advance resource positioning, but system complexity increases due to meteorological data integration requirements
Solution Approach 1:
The system uses universal data processing algorithms that handle multiple data types (weather forecasts, infrastructure data, historical outages) through a single integrated predictive modeling framework. This multi-functional approach reduces the need for separate specialized systems for each data type, managing complexity while maintaining predictive capability.
Data Source
AI summary
A system according to the present disclosure assists utility companies with understanding the potential impact of wet snow and ice accumulations that have the potential to bring down utility infrastructure, such as power lines. The system described herein uses a probabilistic forecast methodology, using weather forecasts as inputs, to develop probable ice and wet-snow accretion predictions and uses those predictions to develop a number of possible events and, in certain embodiments, the events' locations and time of occurrence. The system can provide a probabilistic map of potential impacts to utility lines, thereby giving utility companies the ability to proactively deploy crews before storms arrive.