Avangrid utilities New York State Electric & Gas (NYSEG) and Rochester Gas and Electric (RG&E) are leveraging data analytics to prioritise danger tree management.
The two companies, members of the Iberdrola group, have launched the new initiative to improve the management of what is otherwise a leading cause of outages.
Together NYSEG and RG&E operate more than 68,000km of overhead electric lines across upstate New York, translating to more than 10,600 trees posing an immediate risk to customers.
Historically, the two utilities have addressed danger trees based on imminent risk identified by customer calls, worst performing circuit reports or the local arbourist’s maintenance plans.
This new data driven approach improves on this method with analytics of critical data such as the historical performance of the line, the number of customers served by the line, the number of high-risk trees and the type of protectant on the line to determine a level of priority for removing the danger tree.
The arbourists then create a detailed work plan based on these priorities.
“We’re always looking to innovative our tree maintenance programmes because trees are a leading cause of outages for our customers,” said Pedro Azagra, Avangrid CEO.
“Launching this data driven method has evolved our approach and created many efficiencies, particularly when it comes to danger trees. These dead or decaying trees are outside of the distribution right-of-way but still pose a significant threat to the power grid.”
The new approach, still in the pilot stage, has already delivered results, with the removal of a record number of danger trees in a month and at a cheaper cost.
This in turn should bring benefits for reliability, particularly during the summer storm season.
The approach has been enabled by Avangrid’s data consolidation efforts, integrating data points from across its eight electric and natural gas utilities into one cohesive system.
Avangrid plans to continue evolving its data analytics with tree work by adding more data points, such as the details on tree species, level of tree decay or from the company’s maintenance inspections, and by integrating the company’s computer vision AI system to help identify risks and field conditions.
