Energy Asset Performance Management to Take on Automation

IoT-based asset performance management systems allow utilities and other energy producers to keep a digital finger on the pulse of their generation and distribution operations. The challenge, however, is keeping up with a pulse that beats at lightning speed while sending a steady, voluminous stream of data back to the applications that have to turn those billions of ones and zeros into meaningful, actionable information. 

Collecting and analyzing data at that scale is no small matter, but that capability is at the heart of managing the performance of energy-producing components in real rime. Operators rely on the analyzed and interpreted data to both maintain current operations with effective remedial actions and identify gear that requires maintenance. IoT environments and sensors embedded in components do the collecting and communicating of the raw data while the asset performance management systems had the required analytics based on a variety of criteria.

Health and Maintenance

The amount of data that needs to be sifted through depends primarily on the number and placement of sensors with the energy-generating devices. With data streaming from the installed sensors, most basic asset management systems can report on the health of sensored components — if they’re running properly and at optimal efficiency. 

Initially, these systems could correlate actual performance against anticipated operational benchmarks to gauge the general health of components, which helped the operational staff determine when maintenance was required. But for the most part, while a management system might point to a component and suggest it receive attention, specific decisions such as when to apply maintenance or what subcomponents required attention was largely a manual process.

In fact, some of the more advanced capabilities of available APM systems represent a quantum leap for some utilities and institutional generators.

“For the most part asset performance management is fairly new functionality,” noted Jill Feblowitz, principal of Feblowitz Energy Consulting. “It was conducted mainly on spreadsheets with the exception of a few of the original customers of a company called Meridian, which was acquired by GE.”

Today, with technologies such as machine learning and artificial intelligence available to enhance data analysis, customers expect more from an APM system.

APM Users’ Expectations

Utilities — and even some institutional energy generators — have goals that are similar to those of virtually any business endeavor. They need to operate as efficiently as possible to ensure adequate profit margins which, in turn, will enable them to best meet the needs of their customers. 

So, being able to predict a potential component failure is a key capability that APM systems can offer.

“If they can reduce maintenance costs and head off something ahead of time” the savings could for a utility could be substantial, said Neil Strother, principal research analyst at Navigant Consulting, Inc. “If [a turbine] goes out unawares it could cost them anywhere from a million or a million and half bucks over the course of a week or two to fix it,” noted Strother, “but if they could fix it ahead of time that might save half that or more.”