Enhancing Utilities Operations with Improved Vegetation Management

Curtailing the natural world’s incursion on utility assets is an ongoing battle. Yet the fight cannot be abandoned. The effort to identify and stop encroaching vegetation can prevent electric power outages and keep field workers and customers safe. Still, it is costly and complicated. The good news? Innovation around vegetation management is giving utilities the upper hand.

Vegetation management is one of the largest expenses for transmission and distribution (T&D) companies. “Utilities spend around $6-8b dollars annually on clearing vegetation from overhead lines,” according to Accenture analysis. With many utilities trimming vegetation back on a fixed cycle, the costs of manually surveying power lines and deploying field crews to address potential issues are recurring.

At the same time, the risk of not managing vegetation effectively is increasing. With extreme weather conditions on the rise globally, the dangers of a storm uprooting a tree or starting a utility fire that spreads rapidly to surrounding vegetation are on the rise.

Yet technology is readily available to address these challenges. Applying Artificial Intelligence (AI) and Machine Learning (ML) will fundamentally change how utilities think about vegetation management.

Faster, More Accurate Vegetation Management Analysis

Historically, vegetation management strategies have been time-based. Now, with innovations in AI and ML technology, utilities can analyze visual inspection data faster and more accurately to optimize maintenance planning.

Rather than relying on expensive and time-consuming land-based line patrols, the progressive utility can draw on helicopters, satellites, drones or other aerial methods to gather images, videos, thermal, hyperspectral and multispectral information. It can be overwhelming for the human. However, visual intelligence solutions efficiently centralize and process the raw data, associate images (or point cloud blobs) with specific assets or trees, and extract risk classifications.

Planning teams can:

Tackling the Traditional Expense of Vegetation Programs

Vegetation management is a major OPEX budget line item that is intended to reduce outages, increase compliance, improve safety and reduce the probability of catastrophic events like wildfires. Those are all important objectives, but the price tag need not be so hefty.

AI and ML innovations for visual intelligence improves existing capabilities by improving proper data management pipelines and providing expedited and advanced processing capabilities.

For one, with rapid processing of field crew images, a tree trimming crew can submit work done images and get immediate feedback to move the bucket truck and cut clearance back further. The changes are made all in one trip, without having to send out crews again, for a major cost savings.

Secondly, visual inspections generate large amounts of data. But collected inputs from land-based line patrols can have integrity issues due to inconsistent perspectives, focal accuracies, and distance from the target objects. Using LiDAR to measure electrical corridors, trees, incursions and clearances has become standard industry practice. Yet reviewing these inputs manually is labor and time intensive. It can take weeks or months to correct data integrity issues and cleanse data for legitimate vegetation risk calculation. Digital visual intelligence powered by AI takes on these challenges head-on.

Updating traditional approaches to asset inspection and vegetation management with visual intelligence and asset and vegetation managers’ own experience, utilities can realize benefits including:

The Power of a Visual Intelligence Platform

Supporting the ingestion of all major visual inspection file types, GE Vernova Grid Software's Visual Intelligence platform allows for the optimal blend of image types to drive the highest value. With a built-in AI engine for auto-recognition the platform automatically ingests and processes visual inspection data. This reduces the need for manual quality review tasks.

In addition, the platform provides predictive analytics to deliver value quickly. Applying AI and ML, the utility can auto-identify high risk encroachment areas. The technology draws also on vegetation growth rate data to dynamically create growth models as well as height and volume estimation for vegetation.

Managing vegetation in utility transmission and distribution corridors is key to providing a safe and reliable supply of electricity. With GE Vernova Grid Software's Visual Intelligence platform, integrating with mission critical T&D software (including GIS, ADMS and/or EMS), the utility can scale effective vegetation management to infrastructures many thousands of miles long while reducing costs and cutting risks.