In two blogs for the Utility Analytics Institute, Navigant Research explores how analytics could cut utility costs for vegetation management
Vegetation management — or the prevention of keeping plants and trees from interfering with power lines — can cost utilities billions of dollars each year, and despite accounting for such a large part of the budget, the typical approach is usually routine inspection.
In a pair of blogs for the Utility Analytics Institute, Stuart Ravens, associate director at Navigant Research, explores an alternative approach to routine maintenance — one that relies on predictive analytics, artificial intelligence, and machine learning.
“It was not so long ago that machine learning was touted as the latest technology to improve asset management, and it has since demonstrated its ability to reduce costs and unplanned stoppages,” Ravens said, adding that many utilities are now successfully implementing some form of predictive maintenance in generation and transmission and distribution assets.
But for vegetation management, the approach is not as simple, he said. Vegetation management relies on more varied, complex, and unstructured data, much of which is collected infrequently.
While shifting from routine inspections to a condition-based approach would require far more sophistication, analytics could help utilities save millions of dollars and improve grid reliability, according to the article.
“The complexity of the data involved requires important management, and the complexity of the analytics involved will surpass that of most utility data discovery projects,” Ravens said. “From the outset, utilities will have to pay close attention to model management, data management, and change management to make analytics-based vegetation management a reality.”