In our last article, we introduced our series on utility outage management “before and after the storm.”
This blog continues that series with a look at a key aspect of limiting outage risks before emergencies occur—vegetation management.
Below, we examine why vegetation management is so vital for utilities and how analytics can help utilities perform this work more efficiently and effectively.
Vegetation poses a variety of direct risks to utility infrastructure:
Any of these issues can cause outages (trees are the single most common cause of outages for many utilities), result in safety problems, drive up costs, and even leave utilities open to potential legal liabilities. Falling trees can also exacerbate other outage management challenges by piling more work on crews at the worst possible time: in the middle of a storm.
With these risks in mind, utilities allocate substantial resources to vegetation management programs. Storms can’t be controlled, but vegetation is one area where utilities can proactively invest in limiting the ground-level damage that storms cause. Indeed, Accenture estimates that “Utilities spend around $6-8b dollars annually on clearing vegetation from overhead lines… the largest single operations and maintenance (O&M) expense for most of the utilities in North America.”
In this context, utilities face an imperative to maximize the effectiveness of their vegetation management programs while avoiding extraneous spending wherever possible. As we explore below, analytics can help address this longstanding operational challenge on several fronts.
Like other types of maintenance work, vegetation management tasks like trimming are typically conducted on a fixed cycle (for instance, a given circuit may be trimmed once every 3, 4, or 5 years). They are typically planned by feeder and outsourced to a third-party contractor, who is charged with completing a cycle on time but relies on the utility to manage and prioritize work.
This work is cycled regularly out of necessity and isn’t necessarily aligned with current conditions on the ground. If trees grow back faster near some lines, cause recurring damage in particular trouble spots, or threaten particularly critical assets in certain areas, these crucial operational nuances won’t be reflected in a traditional, static maintenance schedule. For this reason, fixed cycles for trimming and other work can result in redundant efforts and mistargeted maintenance spending.
Predictive analytics can draw on historical asset reliability data to pinpoint the areas where vegetation management work will generate the most possible ROI, avoid wasted effort, and truly rationalize work prioritization. This article from the Utility Analytics Institute examines how PPL leveraged analytics to move to a risk-based maintenance model. A PPL Electric VP notes that “Whereas in years past, someone’s role may have been maintaining equipment, today that person is working with the data analytics team to maintain the algorithm, ensuring it is creating the right inspection and replacement cycle.”
Planning vegetation management work is itself a challenge. Utility professionals are often sent to walk power lines to check for problematic vegetation, determine if customers will need to be notified about the needed trimming work, and modify work schedules as necessary. Some utilities have also begun using drones or camera-equipped trucks to monitor these areas before determining corrective action. Either way, this work can be incredibly time consuming, which means it is not only costly, but difficult to perform regularly for geographically dispersed areas.
Analytics offers new options for vegetation management planning. Satellite imaging can be analyzed quickly and in bulk, using machine learning to compare it with agricultural data and detect encroaching vegetation across broad service areas. Some areas may still need to be checked manually but automating large portions of this labor-intensive process has the potential to drive real savings. This article from T&D World provides an in depth look at how ComEd is combining analytics with technologies like LiDAR and hyperspectral imagery to improve vegetation management outcomes.
Improving vegetation management practices requires integrating data from a number of internal sources, from past storm data, to old maintenance schedules, to years of asset management records. Ideally, it should also integrate dynamic data sources like weather forecasts, seasonal impacts on outage risks, and more.
Most utilities have access to huge stores of data, but the challenge is knowing what to do with it, what can be valuable, and how to realize this value through synthesis with the right external data sources. Finally, a fast, ideally near-real time data infrastructure is needed to make sure vegetation management insights are available to help utility professionals address urgent business questions, like which areas are most at risk from collapsing vegetation in an approaching storm or how a trimming crew should prioritize their work.
This article from Utility Dive provides a great example of why bringing in more data to support vegetation management is so valuable. The impact of vegetation, and its interaction with weather conditions, can change sharply in summer (when leaves act as sails and make trees more vulnerable to wind) versus winter weather (when trees are more susceptible to being broken by the weight of ice).
Vegetation analytics is just one part of what Utility360, HEXstream’s business-ready outage analytics solution, can do. Utility360 offers analytics capabilities tailored to virtually every aspect of outage management, including:
HEXstream is a solutions-driven company dedicated to solving problems and helping utilities get the most value possible out of their data. Our success is rooted in technical acumen, deep domain knowledge, and extensive experience working with some of the largest utilities in North America.