Utilities Turn to Technology to Enhance Critical Vegetation Management Efforts
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- May 31, 2019 5:28 pm GMTMay 31, 2019 5:25 pm GMT
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Vegetation management (VM) has always been a critical component of wildfire prevention, but do conventional methods go far enough? According to an Electric Light & Power article titled “Rethinking Utility Vegetation Management,” “The traditional approach to vegetation management is based on maintaining a required clearance level…. Vegetation clearance requirements by themselves, however, are not sufficient to prevent wildfires or ensure that vegetation does not cause safety and reliability incidents.” That’s why utilities have begun using a variety of technology tools to enhance their VM efforts.
Potential for Dire Consequences
For utilities, failure to deploy a robust VM program can have dire consequences. In an Electric Light & Power article titled “Vegetation management: Not sexy, but vitally important,” VM expert Elizaveta Malashenko is quoted as stating that “utility ignitions are responsible for about 10% of all wildfires in [California].” At a DistribuTECH conference, “she presented research showing the No. 1 cause of wildfires is vegetation contact with transmission lines, with the No. 2 cause being wire/splice failure. Together, these two account for about half of all utility infrastructure fire ignitions.”
That’s why, according to “Rethinking Utility Vegetation Management,” “California has advanced efforts to prevent wildfires with some of the most stringent vegetation management regulations in the U.S.” In fact, “Electric utilities may now disconnect service to customers who refuse to provide access to their property for pruning or removal of trees that pose an immediate threat.”
Given these factors, utilities in California and elsewhere are exploring new ways to approach VM. “Rethinking Utility Vegetation Management” explains that current processes involve relying on VM contractors to determine VM methods. “Often, landscaping decisions are made with no consideration for electric infrastructure. Utilities have no control over vegetation planted outside of their rights of way and generally become aware of vegetation only after it has become a threat.” These factors result in “excessive costs and unclear results.”
Tech-Based Methods for Vegetation Management
Enter technology, including artificial intelligence (AI), analytics, Light Detection and Ranging (LIDAR) data, and specific applications.
According to a blog post by The Utility Analytics Institute titled “Food for thought when feeding machine learning algorithms for vegetation management,” utilities can use AI and analytics for VM. The technology has the capacity to synthesize a collection of images, field records, inspection reports, and other data to make decisions about specific actions to be taken. The author of the post observes that “the decisions AI must make are more complex for VM than in a generation asset: An AI algorithm in a gas turbine will have defined parameters for each data point…. It is more difficult to define the parameters for predictive VM, where algorithms will rely on more subjective interpretation of image data.”
Still, according to “Rethinking Utility Vegetation Management,” “To progress further, utilities must directly engage in the vegetation management decision-making process and use rigorous analytical methods to assess vegetation risks, identify mitigation activities and measure performance.
“The first steps of any vegetation management program should be inspection, data collection and risk assessment.”
One way to improve data collection is through use of LIDAR technology. “If applied correctly,” states “Rethinking Utility Vegetation Management,” “technologies such as [LIDAR] improve the efficiency and effectiveness of identifying dangerous trees and vegetation encroachments, aid in the predictive modeling of vegetation growth patterns, provide comprehensive geospatial geographic information (GIS) right-of-way inventories and assist in identifying high fire risk areas.” The article suggests that utilities perform annual LIDAR surveys as part of their “comprehensive vegetation management programs.”
Software developers such as IBM have recognized the need for this type of technology to assist with VM. Earlier in 2019, the tech giant “unveiled new technology to reduce power outages by helping energy companies predict where trees and other vegetation may threaten power lines…. The system quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of [Internet of Things (IoT)] sensors and weather models…. The resulting insights can help companies…to monitor vegetation growth across their entire service territory, allowing them to better identify and predict infringement with power lines.”
Taking VM to the next level with technological assistance can help utilities save time and money in the long run. However, transitioning to a more tech-based model may be a time- and resource-consuming process. The Utility Analytics Institute, in a blog post titled “The thorny issue of analytics-based vegetation management,” states, “From the outset, utilities will have to pay close attention to model management, data management, and change management to make analytics-based VM a reality. Without strong information management, analytics-based VM could be another analytics project that fails to live up to its initial promise.”
Additionally, utilities must avoid falling into the trap of simply replacing humans with technology. Power Engineering International quotes Malashenko as stating, “It's not really [as simple as] replacing foot patrols with drones.” Rather, she believes that “utilities need to use people for what people are good at and technology for what technology is good at.”
Has your utility employed technology for VM? If so, what have been the results, positive or negative? Please share in the comments.