Overhead powerline vegetation management (VM) is an essential safety operation for
Network Operators, and is amongst the largest maintenance expense for electric companies
(OfGem). Fires, electrical hazards, and asset damage occur without VM, presenting serious
risks to people, property and the economy. For reference, VM-related outages cost the US
$120 billion/year (Electric Power Research Institute).
In the past year, UK outages doubled to 505. Economic cost of one outage can be £0.1bn
(OfGem). Poor VM is responsible for 17% of outages (Eaton, 2014). Penalties levied against
operators are high. In Feb 2014, when 140,000 homes/businesses had no power for 5days,
operators paid £34m just in compensation! Ofgem stipulate higher penalties from 2015.
To plan VM, utility companies need accurate data re what's growing near which asset. There
are 4 methods: manual, ground laser, airborne LIDAR (Light Detection And Ranging),and
LIDAR UAS (Unmanned Aircraft System). All differ in cost, speed (of data collection /post
processing), and usability. There is a growing trend towards UAS. However, because of low
flight (1hr), payload capability, and reliance on time-consuming processing by an expert, UAS
cost/benefit is now also questioned.
Today, only high volt power lines (400/132KV) are mapped, at the rate of 1/5th network
every 5yrs, raising questions as to longterm value of data mapped. Millions of low volt
network miles (33/11KV) are not mapped at all, leaving them without meaningful VM.
This has led to a wealth of global VM opportunities in an industry estimated at $15bn USD
(Gartner), and employing c150,000 people. Over 250 UK companies operate or supply VM
sector.
This project seeks to validate market feasibility for a cost effective long-flight UAS that can
operate autonomously for 8hrs/day, and feed realtime VM data to a bespoke ‘game engine’
that can create ultra-lightweight 3D synthetic environment scans which can be read and
understood by a novice VM planner.