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Stacie Grassano - 2019 Conference Abstract

A practical approach to risk driven Vegetation Management


Because of the typical cyclic nature of Vegetation Management (VM) activity near overhead powerlines, it is difficult to practically and cost effectively predict, identify and address vegetation that has a high likelihood of causing contact, fire ignition or asset failure.  Vegetation that is within cyclic programs is often addressed at the expense of higher risk vegetation.


Networks with high results in performance indicators, such as vegetation outages and fire related starts, are often indicative of VM programs that have struggled with practical and informative risk identification methodologies. Further compounding successful risk driven VM programs is the usage rate of available funding for cyclical work compared with validated higher risk vegetation mitigation works.  Challenges suchas rapid vegetation growth seasons, can expend funding before high risk vegetation is fully addressed.

In order to balance the cyclical nature of vegetation growth and escalate the importance higher risk vegetation mitigation,a dual approach of cycle compliance and risk assignment has been developed.  The methodology allows both compliance to utility codes and regulations to be maintained, as well as an overlay of individual tree, feeder and network risk to be evaluated.  The outcomes allow utilities to practically validate the work which should be done, together with the higher risk vegetation being included and escalated in importance, to ensure a much lower overall risk profile.

The methodology uses data collected from Sensor Based LiDAR programs, together with tree segmentation and meta data attribution to give cost, risk and compliance information for practical VM program implementation.

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