Hazard Tree Detection for Scalable Power Line Risk Assessment
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Solution Overview
Problem
Manual vegetation management for electrical power distribution systems is inefficient, costly, and prone to inaccuracies, leading to potential hazards from hazard trees that can impact electrical assets, with current methods failing to scale and provide reliable risk assessment.
Innovation Solution
A system utilizing convolutional neural networks and generative adversarial networks processes satellite and aerial images to classify hazard trees, determine their heights and distances to electrical assets, and generate notifications of potential hazards, enabling scalable and accurate risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tree inspection is used, then workers can assess tree parameters, but the process is costly, time-consuming, and not scalable
Solution Approach 1:
The patent replaces manual mechanical inspection by utility workers with an automated computer vision system that uses machine learning models to detect and classify hazard trees from satellite and aerial images. This substitution eliminates the need for physical field inspections while maintaining measurement capabilities through automated image analysis.
Solution Approach 2:
The system creates digital copies of trees and vegetation through satellite and aerial imagery, then analyzes these copies using machine learning algorithms to assess hazard levels. This allows remote inspection of numerous trees without physical contact, enabling scalable assessment across large geographic areas.
2Reliability
If manual inspections are conducted over large geographic areas, then hazard trees can be identified, but travel time and costs increase significantly
Solution Approach 1:
The system replaces physical travel to inspection sites with remote sensing technology. Satellite and aerial images provide comprehensive coverage of large geographic areas without requiring workers to travel to each location, eliminating travel time while maintaining hazard identification capabilities.
Solution Approach 2:
The system performs preliminary screening of all trees in the geographic area using automated image analysis before any field actions are taken. This preliminary identification of hazard trees from satellite imagery allows utilities to prioritize and plan field interventions only where necessary, significantly reducing overall time and resource expenditure.
3Loss of information
If manual assessments are performed, then tree parameters can be recorded, but inaccuracies and missed hazards occur
Solution Approach 1:
The system replaces human manual measurement and assessment with automated computer vision algorithms that consistently extract tree parameters from images. This eliminates human error, fatigue, and subjectivity, providing more accurate and consistent measurements of tree height, crown width, and proximity to power lines.
Solution Approach 2:
The machine learning system incorporates feedback loops where model predictions are continuously refined based on validation against known hazard cases and field verification data. This feedback mechanism improves measurement precision over time and reduces information loss by systematically capturing and learning from correction patterns.
Data Source
AI summary
Identifying hazard trees is described. An example method includes receiving multiple images for a geographic area that includes multiple electrical assets of a power distribution infrastructure. Pixels of the multiple images are classified as hazard tree pixels or as non-hazard tree pixels using one or more convolutional neural networks. Multiple polygons are generated based on the hazard tree pixels, a polygon corresponding to one or more hazard trees in the geographic area. A height of a polygon and a distance from the polygon to an electrical asset is determined. Based on the height and the distance, the one or more hazard trees corresponding polygon are determined to represent a potential hazard to the electrical asset. A notification of the potential hazard to the electrical asset is generated and provided.