AI for Smart Grid Vegetation Management

Prioritize trims and patrols using imagery, LiDAR, and risk modeling to reduce outages and wildfire ignition from vegetation contacts.

Vegetation contacts cause outages and wildfires. AI fuses EO/LiDAR, patrol data, and grid topology to estimate clearance, growth, and ignition risk, optimizing trim schedules and patrols.

Weather/fuel dryness and fire danger integration.

Optimized patrol routes and crew scheduling.

Change detection for hazard trees and fall-ins.

live demo · related simulation● LIVE

Patrol/trim history, outage causes, hazard tree records.

LiDAR/imagery processing to canopy models and clearances.

Topology geodata store; span-level identifiers.

Frequently asked questions

What is Regulator/PSPS reporting with before and after evidence?

Regulator/PSPS reporting with before/after evidence.

How should I consolidate topology and patrol data, and what should I label?

The process involves consolidating both the network topology data and patrol information. This consolidation should then be used to identify and label outages and any associated hazards within the system.

What steps are involved in processing LiDAR/imagery for clearance verification?

To verify clearances using LiDAR or imagery, you must first process the data to identify areas requiring inspection. Following this, the results should be validated by comparing them with physical field plots and observations.

How do I train growth and risk models, and what is the recommended pilot approach?

When training growth and risk models, it’s essential to begin with a focused pilot project on a single district. This allows for initial model refinement and prioritization before scaling up across the entire system.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation