The future of utility vegetation risk modeling


By Shane Brunker, Technical Director at NM Group

The way we quantify the threat that vegetation poses to an electricity network is changing. Emerging technology and a strong customer desire for more effective work prioritization, compliance and cost reduction is driving the change. Over the course of three blogs I wanted to outline my thoughts on the new approach, talk about key components of the model and look at how it might be implemented in practice.

Why look at risk modeling?

Most electrical networks with overhead infrastructure are subject to threats from falling or growing vegetation, with network operators spending significant sums in scoping and managing to inspect and manage easements/corridors. However, it can be argued that this effort is focused on maintaining compliance, rather than managing the overall risk as effectively as possible with the budget available.

I believe much greater insight into vegetation risk is available to utilities, through the use of remote sensing technology paired with the right suite of analytics. It is not enough to simply highlight where there is vegetation in the vicinity of a line, or even within a minimum 3D distance. We should be able to describe why the likelihood of an incident is higher on a particular span, and how severe any impact might be. This gives us a combined risk score and therefore a clear and quantifiable means by which work can be planned and prioritized.

So what does this mean in practice?

We are advocating a risk-based approach based on likelihood models for tree growth and fall, which are in turn derived from sources such as LiDAR, meteorological patterns, historical statistics, hyperspectral imaging and ground verification studies. This means challenging generic programs for corridor management and using the risk information to modify where work is done and on what cyclical basis. It means creating the links between this risk-based scoping and the work program development, balancing work efficiency with risk reduction.

It’s important to note that this approach is based on a range of new analysis techniques but is also grounded in LiDAR and forestry literature. It has been incorporated into a 2 year research program focused on utility vegetation risk, led by our researcher Sophie Davison and in collaboration with an academic partner. The tools are generic, but require configuration for each specific network and its associated geography, policies and regulatory environment.


In summary, by pairing reliable and tested models of likelihood, and applying this against a consequence metric based on factors such as the number of connected customers and the fire risk-rating, we believe that we can reduce the chance of vegetation-derived outages or reduce the cost of managing trees – or both. In the next part of this series I will look at some of the components that make up the model and look at how these form part of the broader solution.

Read more about vegetation analytics here.