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This paper presents a novel data-driven approach for predicting the number ofvegetation-related outages that occur in power distribution systems on amonthly basis. In order to develop an approach that is able to successfullyfulfill this objective, there are two main challenges that ought to beaddressed. The first challenge is to define the extent of the target area. Anunsupervised machine learning approach is proposed to overcome this difficulty.The second challenge is to correctly identify the main causes ofvegetation-related outages and to thoroughly investigate their nature. In thispaper, these outages are categorized into two main groups: growth-related andweather-related outages, and two types of models, namely time series andnon-linear machine learning regression models are proposed to conduct theprediction tasks, respectively. Moreover, various features that can explain thevariability in vegetation-related outages are engineered and employed. Actualoutage data, obtained from a major utility in the U.S., in addition todifferent types of weather and geographical data are utilized to build theproposed approach. Finally, by utilizing various time series models and machinelearning methods, a comprehensive case study is carried out to demonstrate howthe proposed approach can be used to successfully predict the number ofvegetation-related outages and to help decision-makers to detect vulnerablezones in their systems.

A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems