Electric Grid Damage Assessment Using Lidar and 3D Virtual Models
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Solution Overview
Problem
Current methods for assessing damage to electrical power grid infrastructure after storms are labor-intensive, costly, and inefficient, relying heavily on manual inspections and lacking effective automated systems for damage identification and safety hazard detection.
Innovation Solution
Utilizing lidar technology for automated damage assessment, integrating lidar data with machine learning algorithms and virtual model systems to create a 3D virtual model of the grid infrastructure, enabling automated inspection and classification of damaged assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to identify damage locations, then inspection accuracy can be maintained through human expertise, but inspection time and costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection with automated aerial vehicles equipped with sensors (lidar, cameras, thermal imaging) to capture and analyze infrastructure data. This substitution maintains detection accuracy through advanced sensing technology while dramatically reducing inspection time by automating the data collection and analysis process across hundreds of miles of infrastructure.
Solution Approach 2:
The patent creates virtual copies or digital twins of the physical infrastructure by capturing detailed sensor data and generating 3D models. These digital replicas allow for automated analysis and damage identification without requiring physical inspection, thereby reducing time loss while maintaining measurement precision through comparative analysis of pre and post-event virtual models.
2Area of stationary object
If extensive manual teams are deployed to inspect widespread infrastructure damage, then comprehensive coverage can be achieved, but labor costs and operational complexity increase
Solution Approach 1:
The patent employs multi-functional aerial vehicles that can perform various inspection tasks including visual inspection, lidar scanning, and thermal imaging. These vehicles serve multiple purposes: data collection, navigation along predefined routes, automated analysis, and communication. This universality allows comprehensive coverage of large areas without proportionally increasing system complexity, as a single platform performs multiple functions.
Solution Approach 2:
The inspection system is designed to be self-sufficient, with aerial vehicles autonomously navigating predefined routes, collecting data, analyzing images and sensor information, and transmitting results without requiring constant human intervention. The vehicles self-manage their inspection tasks, reducing the need for complex human coordination and operational management while achieving extensive coverage.
3Loss of information
If traditional monitoring equipment is used, then real-time data can be obtained, but the equipment is expensive, rarely deployed, and becomes ineffective when circuits are de-energized
Solution Approach 1:
The patent performs preliminary data collection by capturing images and sensor data before infrastructure damage occurs or before circuits are de-energized. Aerial vehicles conduct pre-storm baseline inspections and post-storm assessments, storing this data for later comparison. This preliminary action ensures that information is captured when infrastructure is accessible and functional, avoiding the need for complex real-time monitoring equipment that fails when power is lost.
Solution Approach 2:
The patent uses aerial vehicles as intermediaries to bridge the gap between infrastructure monitoring needs and the limitations of traditional equipment. These vehicles carry multiple sensing capabilities (visual, lidar, thermal) that can operate independently of circuit power status. The aerial platform mediates the inspection process by collecting data from areas where fixed monitoring equipment cannot operate, particularly when circuits are de-energized or damaged.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates rapid, cost-effective identification of damaged grid components, reducing manual labor and improving post-storm recovery efficiency by providing a comprehensive virtual inspection of power grid infrastructure.
Implementation Method 1
a lidar sensor carried on a flight vehicle to scan the grid infrastructure
Implementation Method 2
lidar technology for automated damage assessment
Data Source
AI summary
A computer-implemented method for automated damage assessment of electric grid infrastructure for post-storm recovery includes a virtual model system including a three-dimensional (3D) virtual model of physical grid infrastructure with assets, using a lidar imaging data point cloud collected in the field to characterize the physical asset condition, performing a simulation algorithm including a damage simulation and a lidar imaging data simulation, and executing a trained classifier to identify the damaged or undamaged condition of the asset using the field collected lidar imaging data point cloud, where the classifier is trained with simulated collected lidar imaging data of undamaged and damaged assets.