3D Power Grid Wire Modeling From LIDAR Surfel Data
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Solution Overview
Problem
Current electrical power grid modeling approaches inaccurately predict the locations of important assets like transformers, capacitors, and power lines due to the growing complexity and variability of power grids, necessitating a dynamic modeling and monitoring system.
Innovation Solution
A system utilizing surfel data from LIDAR ranging data to accurately detect and classify the locations of electric grid assets, including power lines and utility poles, by converting ranging data into surfel images and applying image processing techniques to generate a polyline graph and identify asset locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 2D image processing is used to detect grid assets, then the system is simpler to implement, but it cannot distinguish occluded objects and provides inaccurate location predictions
Solution Approach 1:
The patent transitions from 2D overhead imagery to 3D surfel representations by incorporating LIDAR ranging data. Each surfel contains depth information and surface normal vectors, enabling the system to distinguish occluded objects and accurately predict asset locations in three-dimensional space, directly resolving the limitation of 2D image processing.
Solution Approach 2:
The system combines multiple data sources (overhead imagery, LIDAR ranging data, surfel attributes) to create a composite representation of the power grid environment. This multi-source fusion approach improves measurement precision by leveraging the complementary strengths of different sensing modalities while managing the complexity through integrated processing.
2Measurement precision
If surfel data from LIDAR is used to detect grid assets, then the system can distinguish occluded objects and improve location accuracy, but the data processing complexity increases
Solution Approach 1:
The patent segments the LIDAR point cloud data into discrete surfel units, each representing a small surface element with specific attributes (position, normal vector, depth). This segmentation allows the system to process complex 3D data in manageable units, applying classification algorithms to individual surfels and then aggregating results to detect complete assets, thereby reducing overall processing complexity.
Solution Approach 2:
The system transforms raw LIDAR ranging data into surfel representations by changing the parameter space from distance measurements to surface element attributes (position, orientation, depth). This parameter transformation enables the application of image processing techniques to 3D data, simplifying the detection process while maintaining high measurement precision.
3Adaptability or versatility
If the system models the entire power grid at once, then the model is comprehensive, but it becomes difficult to manage and update dynamically
Solution Approach 1:
The patent divides the power grid into discrete asset instances (transformers, capacitors, power lines) and models them as separate detectable objects rather than a monolithic system. This segmentation enables independent detection, modeling, and updating of individual assets, making the overall system more adaptable and easier to manage dynamically while maintaining comprehensiveness.
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
The system provides a more accurate representation of power grid structures and assets, enhancing predictive capabilities and operational monitoring by distinguishing occluded objects and accurately mapping grid wires and poles.
Implementation Method 1
ranging data such as light detection and ranging (LIDAR) data
Implementation Method 2
light detection and ranging (LIDAR) data
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a storage device, for electric grid modeling using surfel data are enclosed. An electric grid wire identification method includes: obtaining a set of surface elements (surfels), wherein each surfel of the set of surfels represents a portion of a surface of an object in a geographic region; selecting, based on one or more surfel attributes, one or more surfels of the set of surfels that each represent a portion of a surface of an electric grid wire; generating a representation of the electric grid wire from the selected one or more surfels; and adding the representation of the electric grid wire to a virtual model of the electric grid. Obtaining the set of surfels can include obtaining ranging data of the geographic region; and generating the set of surfels from the ranging data.