5 practical steps to reduce fall-in risk now


While utilities have traditionally focused on managing vegetation growth, fall-in trees have quietly become the leading cause of tree-related power outages.

Today’s climate challenges — intensified storms, drought, and pest infestations — are compromising tree health and increasing mortality, which heightens the risk of trees falling onto power lines. In some regions, robust winds are even toppling healthy trees.

Such fall-ins disrupt utilities’ ability to deliver safe, reliable, and affordable power. They reduce service reliability as customers face outages, and drive up costs through necessary restorations and repairs. Moreover, when a fall-in coincides with a downed line, it can ignite wildfires, leading to dire consequences.

Today’s Anecdotal Approaches to Managing Fall-Ins

Currently, without a strategic plan or dedicated funding, the approach to mitigating fall-in risk involves relying on anecdotal information and chance. Utilities often become aware of potential fall-in risks through:

  • Work crews noticing issues during other tasks.
  • Customers reporting problems on or near their properties.
  • Passersby spotting issues.

Since costs are rising but budgets aren’t, even reported risks often go unaddressed. These trees end up on backlog lists and are only tended to when crews have spare capacity, impacting SAIDI and SAIFI metrics unfavorably.

With this high number of backlogged risks to address, response prioritization — determining which results of downed lines will be more impactful — is essential

National Grid tackles unwieldy trim cycles and fall-ins

This utility takes a data-driven approach to vegetation management and trim cycles using satellites and AI. AiDash IVMS™ analysis helps pinpoint locations at risk of falling trees,  develops a list of priority hotspots for hazard tree removal, and helps National Grid to:

  • Unlock millions in vegetation management efficiencies — over $5 million per year that can be redirected to critical risk areas.
  • Improve system SAIFI by around 5%.
  • Decrease and optimize routine maintenance line miles by 16%.

A new strategy is necessary, starting with a structured framework.

Framework to reduce fall-in risks: 5 steps

  1. Identify high-risk trees: Determine which trees are most likely to fall based on height, health, proximity to power lines, slope, soil conditions, and wind exposure.
  2. Assess impact: Evaluate how each fall-in risk could affect system reliability, resilience, and safety.
  3. Prioritize remediation: Focus on addressing the trees that pose the greatest risk of causing outages.
  4. Operationalize and streamline processes: Increase operational efficiency to remove more trees per dollar spent. Intelligently group work plans for efficient execution.
  5. Measure and deliver feedback: Collect and analyze data from these efforts to improve future operations.

Satellites and AI: The new approach to managing fall-ins

Outside of traditional right-of-way areas, assessing tree health and determining the exact strike zone is critical. Satellite technology, enhanced by AI, is the only method capable of quickly and accurately assessing fall-in probabilities across extensive areas and calculating potential impacts.

While LiDAR can identify at-risk trees, satellite technology surpasses LiDAR in detecting unhealthy trees through advanced imaging techniques. Using broad spectrum wavelengths and near infrared spectroscopy, satellite remote imaging can measure chlorophyll and moisture content to determine vegetation health data for early risk warning.

In addition, costly LiDAR provides a snapshot in time, while less expensive satellite surveys repeatedly recheck areas to stay abreast of changes. By the time a hazard tree is scheduled to be addressed based on a static LiDAR snapshot, the tree may have already fallen.

Risk = Probability of Fall-in x Impact of Outage

AiDash approach to determining risk =
Probability of fall-ins as revealed by satellite imagery and AI x Impact as defined by criticality score

How AiDash approaches fall-ins

Using satellites and AI, AiDash Intelligent Vegetation Management System™ (IVMS™) scales to cover thousands of line miles yet homes in to collect individual tree-level detail.

With an AI model informed with data from more than 125 utilities and 3 million+ T&D lines processed, IVMS has developed insights into vegetation management that can reduce vegetation management expenses by 20-30% and gain 10% better grid reliability — helping utilities do more with less.

To support the 5 steps to mitigate fall-in risk, IVMS will:

  1. Identify high-risk trees.
    • Creates a digital surface in elevation model. IVMS surveys the digital surface to determine where the assets and trees are located, then measures the height of trees. Evaluating these numbers with topography, IVMS determines existing clearances and the trees at risk of falling into the potential strike zone.
    • Quickly inspects and digitizes every tree throughout an entire network, both inside and outside the ROWs.
    • Identifies trees at risk of fall-in, both healthy and unhealthy, by referencing satellite data and reports from work crews and customers. Uses broad spectrum wavelengths and near infrared spectroscopy to measure chlorophyll and moisture content to determine vegetation health.
    • Assesses probability of fall-ins. IVMS also considers species and adverse conditions, such as a lack of windbreaks, tree lean and ill health, loose soil conditions, and storm-prone regions, to determine probability.
  2. Assess impact.
    • Assigns criticality scores. IVMS provides a critical ranking of at-risk trees that is specific to your regional challenges and concerns.
      • Considers probable outage impact.
        • Downstream impact of customer interruptions and increases to SAIDI and SAIFI.
        • Critical infrastructure affected — for example, a water treatment plant, hospital, or school.
      • Considers probable wildfire impact.
        • What would the tally be of acres burned, buildings destroyed, lives lost, and the financial burden?
        • Weighs effects on customers — a population dense housing area compared to a rural zone.
  3. Prioritize remediation.
    • Prioritizes hazard trees for removal based on criticality scores — the customer impacts of an outage on each feeder segment.
  4. Operationalize and streamline processes.
    • Digitizes workflows. IVMS drives operational efficiency, replacing spreadsheets, handwritten data collection, and untold planning meetings with modern analytical capabilities and using automation to build work plans. IVMS analyzes current and historical data, to specify when and how to address the most trees per work hour.
    • Gets crews to the right place at the right time by integrating work management software with its field app for direct collaboration and Google Maps for intelligent routing, reducing mobilization costs and windshield time.
  5. Measure and provide feedback.
    • Generates reports to provide data-driven attributions of budget objectives and needs to management, executives, regulators, and customers, and to confirm, monitor, and audit work completion (especially for areas that are difficult to access).
    • Continually “closes the loop,” improving insights by returning these results to the system for AI analysis.

AiDash IVMS equips utilities to move beyond anecdotal methods, targeting and mitigating fall-in risks effectively through a structured, data-driven approach.

Learn more about how IVMS can transform your vegetation management strategies or book a demo today.

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