Predictive Risk Management for Dynamic Tree Trimming Scheduling for Distribution Networks

This paper introduces a predictive method for distribution feeder vegetation management based on a risk framework. The state of risk is calculated for each feeder section using a variety of factors extracted from network parameters and historical outage data, historical weather data and weather forecasts, and a variety of vegetation indices. The framework implements the spatiotemporal correlation of all the collected data. The prediction model used is the Gaussian conditional random field, which takes into account spatial interdependencies between different feeder sections. This enables better prediction accuracy, and also offers the capability to deal with missing and bad data. Based on the calculated risk, the dynamic optimal tree trimming schedule, which minimizes the overall risk for the system under a given predetermined budget, is developed. Results obtained on a real utility network show that optimal tree trimming based on the developed risk framework for vegetation management could significantly decrease the overall risk of the feeder outages without increasing the budget.