Prediction of distribution feeder outages caused by storms

每年秋季和冬季，不列颠哥伦比亚省(BC)的下大陆都会经历暴风雨和极端天气。严重的暴风雨可能会摧毁树木和配电馈线，导致数千名客户在几个小时到几天内停电，迫使学校关闭，公共交通系统中断，生产线关闭。这个问题在冬天尤其严重，因为停电意味着在寒冷的天气里没有暖气。风暴造成配电网馈线中断的原因是多方面的，包括天气、环境和物理馈线条件。在风暴事件中，大风是馈线中断的主要原因，但并不是唯一的原因。配电馈线可能会因闪电、降雪、结冰、暴雨和树木倒下而发生故障。树木倒下与风有关，也与植被的接近和健康有关。在风暴中，老化的馈线可能有更高的停机概率。 本项目的目标是开发一种建模方法，并讨论相关数据问题，以便在未来风暴之前预测配电网馈线故障。这样的提前预测将为现场管理人员提供几个小时到几天的应急准备和响应时间，从而减少风暴造成的社会、经济和环境损失。这是一项具有挑战性的任务，因为涉及的因素很多，而且这些因素具有不确定性。BC Hydro和SFU将合作应用数据挖掘技术和统计方法来解决这个问题。该方法利用历史中断数据、天气数据库和馈线信息的组合，以定量的方式提取馈线中断与多种因素之间的关联。然后，将这种相关性与天气预报结合使用，以预测下一场风暴之前的馈线中断。该项目将为数据挖掘和统计研究贡献新的知识，以处理散布在多个数据源中的、固有的不确定和噪声的非结构化数据。

Every year, the Lower Mainland in British Columbia (BC) will experience storms and extreme weather in fall and winter. Severe storms could knock down trees and distribution feeders, causing power outages for thousands of customers over several hours to several days, forcing school closure, interrupted public traffic systems, and shutting down production lines. This problem is especially serious in winter because power outages mean no heating in cold weather. A distribution feeder outage caused by storms is due to multiple factors, including weather, environment and physical feeder conditions. High wind is a major cause of feeder outage in a storm event but is not a unique one. A distribution feeder can fail due to lightning, snow, icing, heavy rain and tree falling. Tree falling is related to wind and also to the proximity and health of the vegetation. An aged feeder may have a higher outage probability in a storm. The objective of this project is to develop a modeling method and discuss relevant data issues for the prediction of distribution feeder outages ahead of a future storm. Such an advanced prediction will give field managers a lead time from several hours to several days for emergency preparation and response, consequently reducing social, economic and environmental damages of storms. This is a challenging task because of the number of factors involved and the uncertainty of these factors. BC Hydro and SFU will collaborate to apply data mining techniques and statistical methods to solve this problem. The approach is to leverage a combination of historical outage data, weather databases, and feeder information to extract correlations between feeder outages and multiple factors in a quantitative manner. Such correlation is then used in combination with the weather forecast to predict feeder outages prior to the next storm. This project will contribute new knowledge to data mining and statistics research in dealing with unstructured data that are scattered in multiple data sources and are inherently uncertain and noisy.