By Rod Walton, Senior Editor
|photo credit- Landis+Gyr.|
An enormously successful business tycoon was once described as someone “who could see around corners.” He or she prospered because they saw the future, and acted upon it, before calamity or opportunity happened. Hindsight may be 20/20, but foresight is worth millions and maybe billions.
The fact that data analytics is reshaping the way utilities go about their own business is hardly a stop-the-presses moment (and even that phrase itself is fast becoming an anachronism). Those monitoring and sensor devices have become omnipresent, telling the electricity providers almost instantaneously when a circuit has blown or a transformer is out, and that quick response means reduced outage times and more appreciative customer satisfaction.
But cutting-edge metering and predictive analytics are taking an infinite step forward. They keep vigil on danger signals coming from equipment and provide a heads-up so that outage times can be avoided in the first place. They try to see around the corners, so to speak.
“Poor equipment reliability leads to outages and unplanned downtime resulting in lost revenue and unsatisfactory customer service,” reads a white paper from global utility infrastructure software firm Bentley Systems, entitled “Asset Performance Management: Bridging the Gap Between CAPEX and OPEX,” available on power-grid.com.
“Low asset efficiency leads to low throughput and utilization,” author Sandra DiMatteo wrote in that paper. “Many owner-operators find that reactive maintenance and corrective action are several times more expensive than proactive, planned maintenance.”
But isn’t it human nature not to look for trouble? If it ain’t broke, don’t fix it. What the explosion in analytical technology is telling us, however, is that it will break and we have a pretty good idea when and where. Only a planner with a death wish for obsolescence would not want to do the fixing at his or her company’s convenience.
Another report by metering and energy management firm Landis+Gyr, noted that in the past utilities relied on word of mouth or other manual processes to identify and act on faulty equipment or damage issues. Author Kent Hedrick, Landis+Gyr’s director of grid management solutions, made the case that advanced grid analytics already are delivering value for utilities via improved resiliency, asset management, outage restoration and theft prevention and are ready for storage technologies and solar power distribution.
“The role of distribution grid analytics will only grow in importance,” Hedrick wrote. Those new capabilities include “spatial visualization of the distribution grid, more accurate and sophisticated planning, better operational performance and improved asset management. These capabilities enable utilities to leverage historical or stored data (data at rest) to automate formerly manual processes for grid planning purposes.”
Prad Tripathy, a colleague of Hedrick at Landis+Gyr in charge of solutions product management, told POWERGRID International that meters can measure load levels at the grid edge and determine the aggregate load at distribution transformers. The grid analytics can identify any anomalies.
“For example, analytics based on transformer load can help (utilities) understand abnormal loading patterns and consequent deterioration of the life of distribution transformers, and can also be used to confirm proper transformer sizing,” Tripathy said. “Beyond identifying potential asset failure, analytics are useful in identifying other causes of unplanned outages. Vegetation or animal contact with distribution equipment can cause momentary outages which might eventually lead to a sustained outage. Grid analytics monitor and identify momentary outages, and this data can be further evaluated by system planners to plan for remedial steps such as tree-trimming or deploying animal guards.”
Skeptics, perhaps those who have to judge whether capital spending on analytics is done wisely and ensures a decent return on investment, may wonder whether the ever-adapting software is accurate. How can software-or more importantly, us mere mortals-make sense of millions of bits of data? Thus, extremely advanced mathematical algorithms are paramount to run predictive analysis, Tripathy said.
“Advanced algorithms like regression analysis and machine learning can and do perform reliably,” he said. “We recommend starting with practical use cases that show real value to the utility and then build from there. This process provides confidence in the results.”
Meters play a key role in predictive analytics. Don Bowman, Wake Electric’s manager of engineering, said meters provide consumer usage data that can help providers analyze that information to predict future trends.
Data analytics enables utilities to retrieve information from various sources, turning data into actionable insights to improve operations, reduce cost and enhance customer service.
photo credit- Sensus
In addition, meters coupled with analytical software can detect danger signals from equipment in the field. Wake Electric has used the Sensus transformer utilization application to get real-time notifications on voltage issues, which extended transformer life, Bowman said.
“With the alarms, utilities can easily see if a transformer needs to be changed,” he said.
The most important work of predictive analytics in the electricity delivery sector is to balance peak demand, flatten the load curve and get the most efficiency out of generation sources, said Brian Crow, Sensus’ vice president of analytic solutions. These bits of forward thinking information, taken in context, can help reduce costs, push back the need for new generation construction and allow distributed generation technologies to catch up to the “current realities,” he said.
One device or type of analytics does not fit every need, however. In addition, technology still has far to go to bring predictive order to the certainty that uncertain things will inevitably break down.
“The greatest need is for more granular data at points along the grid-not just from meters,” Crow said. “By deploying more smart sensors along the distribution network, public service providers can better manage distributed energy resources and monitor voltage, frequency, harmonics, reactive power and more.”
Aside from Bentley, Landis+Gyr and Sensus, companies that have invested in the predictive analytics playing field include DNV GL, AutoGrid, GE Automation & Control and Itron, among others.
Why the shift? It’s the economy, stupid. Seeing the impact of analytics among fast-rising Internet giants such as Amazon and Google helped utility leaders see where the world was going.
“Innovations incorporated into predictive controls technology software and learned in the world of e-commerce have helped make the analytics more intelligent and self-optimizing,” said Dr. Amit Narayan, founder and CEO of AutoGrid.
Smart meters, working with predictive asset management technology, can help utilities determine if equipment failure might lead to a system outage sometimes four hours to a week before it actually happens. This helps with scheduling maintenance and repair crews on a proactive, cost-efficient basis, Narayan said.
“Because utilities are able to sense when a transformer or some other grid asset might break in the future, they can send maintenance crews to fix it beforehand,” he said. “A final impact is improved customer satisfaction and safety.”
The bottom line is this reduces outages. Here’s how utility pros see the math of predictive asset management: reduction of outages means money saved and customers kept happy. Indeed, the U.S. Department of Energy has estimated that power outages and interruptions cost Americans at least $150 billion per year.