Quantifying the risks of tree failure to guide proactive management and increase the resilience of electricity distribution networks.

严重的风暴导致树木连根拔起和折断，这反过来又会对电力供应网络造成相当大的破坏。例如，在2013/2014年圣诞节期间，英格兰东部有超过50万客户断电超过5天，这主要是由于暴风雨造成的灾难性树木故障导致架空电力线受损。随着气候变化导致风暴变得更加频繁和严重，这种类型的破坏可能在英国变得更加普遍。通过砍伐那些靠近电力线、在严重风暴中更容易倒下的树木，可以减少风暴对电网的影响。然而，目前对采油树失败可能性的评估是由测量员完成的，他们根据对采油树的实地观察做出主观和定性的个人判断。这种方法缺乏一致性和科学严谨性，可行评估的树木数量和重复调查的频率受到后勤和财政限制的限制。该项目将通过开发一种新的方法来评估在恶劣天气下单株树木失败的风险，从而解决这些限制。这将从开发计算机软件开始，该软件可以在一个可能包含数千棵靠近输电线的树木的景观中优先考虑最有可能失败的树木。然后，那些风险最大的树木将被针对进行更详细的测量，特别是使用激光扫描仪，这将使我们能够对单个树木的失败风险提供更现实和客观的评估。我们将与我们的项目合作伙伴苏格兰电力公司合作，展示这种新方法如何能够帮助他们将资源用于管理失败风险最大的树木，以增加恶劣天气下电力供应网络的恢复能力，并最大限度地减少对客户的干扰。在这项研究中开发的技术对于改善靠近其他基础设施（如道路、铁路和建筑物）的树木的管理将是有价值的，从而有助于减少风暴造成经济损失、服务中断和对人类伤害的可能性。

Severe storms lead to the uprooting and breakage of trees and this, in turn, can cause considerable damage to electricity supply networks. For example, over the Christmas period 2013/2014 over 500,000 customers were off supply for over five days in the East of England and this was mainly due to damage to overhead power lines caused by catastrophic tree failure in storms. This type of disruption is likely to become more common in the UK as climate change causes storms to become more frequent and severe. It is possible to reduce the impacts of storm damage on electricity networks by felling those trees that are close to power lines and are at more risk of failing in a severe storm. However, assessment of the likelihood of a tree failing is currently done by a surveyor who makes a subjective and qualitative personal judgement based on a field observation of the tree. This approach lacks consistency and scientific rigour and the number of trees that it is feasible to assess and the frequency of repeat surveys is restricted by logistical and financial constraints. This project will address these limitations by developing a new approach to evaluating the risk of failure of individual trees in severe weather. This will begin by developing computer software which can prioritise trees at greatest risk of failure across a landscape which may contain thousands of trees that are close proximity to power lines. Those trees at greatest risk will then be targeted for more detailed measurements, particularly using laser scanners, which will allow us to provide more realistic and objective assessments of the risk of failure of individual trees. We will work with our project partners Scottish Power to demonstrate how this new approach is able to help them target their resources for managing trees at greatest risk of failure, in order to increase the resilience of electricity supply networks in severe weather and minimise disruption to customers. The techniques developed in this research will be valuable for improving the management of trees that are in close proximity to other infrastructure such as roads, railways and buildings, thereby helping to reduce the likelihood that storms will cause financial losses, disruption to services and harm to humans.