Prediction of distribution feeder outages caused by storms

每年，不列颠哥伦比亚省（BC）的低地都会在秋季和冬季经历风暴和极端天气。严重的风暴可能会击倒树木和配电馈线，导致数千名客户在数小时至数天内停电，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。由风暴引起的配电馈线停电是由多种因素造成的，包括天气、环境和物理馈线条件。大风是风暴事件中馈线停运的主要原因，但不是唯一原因。配电馈线可能由于闪电、雪、结冰、大雨和树木倒塌而故障。树木倒下与风有关，也与植被的接近和健康有关。老化的馈线在风暴中可能具有更高的中断概率。 该项目的目标是开发一种建模方法，并讨论相关的数据问题，用于预测未来风暴前的配电馈线停电。这种先进的预测将使现场管理人员有几个小时到几天的准备和应对紧急情况的时间，从而减少风暴对社会、经济和环境的破坏。这是一项具有挑战性的任务，因为涉及的因素很多，而且这些因素的不确定性也很大。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。该方法是利用历史停电数据，天气数据库和馈线信息的组合，以定量的方式提取馈线停电和多个因素之间的相关性。然后，这种相关性与天气预报结合使用，以预测下一次风暴之前的馈线中断。该项目将为数据挖掘和统计研究提供新的知识，以处理分散在多个数据源中的非结构化数据，这些数据具有内在的不确定性和噪声。

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.