IIOT Power

GE Launches New Analytics Technologies to Boost Grid Efficiency 

General Electric (GE) has rolled out a new portfolio of predictive analytics that could allow utilities to use data from transmission and distribution networks to achieve better operational efficiency as more distributed assets are introduced to the grid. 

The company on June 11 unveiled three new grid analytics—for storm readiness, network connectivity, and effective inertia—that it said will “combine domain expertise with artificial intelligence (AI) and machine learning to tackle pressing challenges in electric grid operations.” 

The storm readiness analytic uses high-resolution weather forecasts, outage history, crew response, and geographic information system (GIS) data to forecast storm impact and prepare response crews and equipment ahead of impending weather. In a statement, Brian Hurst, a vice president and chief analytics officer at Exelon Utilities, said the offering was promising. “When it comes to storm restoration, it will enable the utilities to become more surgical in prepositioning crews in advance of weather events – saving time, money, improving customer satisfaction and enhancing safety for employees,” he said.

“We are just beginning to scratch the surface on the value of analytics, and when we look at distributed energy resources and the Internet of Things, it becomes increasingly important for the future.”

The network connectivity analytic uses GIS and other operational system data to detect, recommend, and correct data errors, which crop up when information is manually inputted at the customer or equipment level. “Armed with better data, utilities can more efficiently dispatch crews, reduce outage restoration time and avoid incorrect outage notifications to customers,” GE said. 

The effective inertia analytic provides enhanced visibility into transmission networks, whose operation is growing increasingly complex as shares of renewable generation  on the grid increase. “This has led to a massive displacement of ‘system inertia,’ or the resiliency of power generation, given spikes in customer demand or reduced supply, due to unforeseen decreases in wind or sunlight,” GE explained. The analytic uses machine learning to help measure and forecast system inertia to enable a more stable grid. 

The analytics are connected via GE’s Digital Energy data fabric—essentially a platform that functions as a source of integration for different applications for a simpler, yet more dynamic, management of the grid. 

GE is one of several companies developing digital technologies that promise to transform transmission and distributed systems from their traditionally reactive mode of operation to one that is more predictive, and even prescriptive. Some experts suggest that in the long term, AI and machine learning could substantially reduce human intervention in daily grid operations. 

GE’s transmission and distribution digital portfolio—which includes offerings like intelligent substations, regional automation systems, and advanced network operations centers—is part of a larger and ever-expanding suite of tools the company has developed for the Industrial Internet of Things (IIOT). The company forayed into the relatively IIOT market in 2012, when it launched a $1 billion investment in software and analytics. In 2013, it developed its first software platform for IIOT, and in 2014, it launched Predix, a cloud-based operating system for building and powering industrial-strength applications. In 2018, the company moved to fully integrate digital technologies across the entire spectrum of GE products and services. 

Steve Martin, acting CEO for GE Digital, and GE Power’s chief digital officer, on Tuesday noted analytics will be integral to future grid systems, which are absorbing more and more smaller, decentralized power systems and need to increase the levels of bi-directional electricity flows on networks. “The energy industry today is leveraging a small fraction of their operational data,” he noted. 

“Our grid analytics enable utilities to use more of that data and orchestrate their networks and the workers who operate them in ways previously unimagined—not only for current processes, but also for future unforeseen scenarios,” he said.

—Sonal Patel is a POWER associate editor (@sonalcpatel, @POWERmagazine)

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