refining-big-data-energy-industry

Refining Big Data in the Energy Industry Into Role-Specific Insights

Lawrence Willey

Big data in the energy industry can enable better decision-making, but it must first be disseminated efficiently to serve the needs of those who use it.

The Industrial Internet of Things (IIoT) is the next major digital movement gaining momentum in the power-generation industry, spurring increased adoption of big data beyond its current applications in utilities on the distribution and power delivery side of the system. This utilization of big data in the energy industry is leading to impressive results, including improved profitability, better equipment utilization, and faster delivery of more useful information to those who can benefit from it the most. In order to distill and share relevant insights from that mass of information with team members, however, it's critical to use simple, effective methods and tools.

Aggregating the Data

Rapid advances in wireless technologies are changing the way controlled systems are monitored. The most widely adopted systems currently emerging involve sensors collecting data at the equipment and plant levels. Then, the plant-level data is aggregated to an enterprise data warehouse and, in turn, this data pipe flows to the cloud. Data analysis and the very latest pattern recognition and "digital twin" algorithms are run in the cloud using various applications. These apps are highly customized solutions for particular utilities and functions, and many are available from platforms similar to app stores for Android or Apple.

Making Sense of the Data

Magin Reyes, the director of fleet operations for wind and solar at Exelon Energy, speaks to how these advances are yielding significant results. Exelon is now able to take advantage of weather forecasting that includes power-generation forecasting to feed grid-supply decisions for their wind and solar fleets. The ability to use advanced, adaptive data analysis methods that cross functional boundaries also results in more concise data summaries tailored for the downstream users. One example would be finance data coupled with parts consumption and turbine operational parameters that yield everything from proposing warranty reserve revisions that include business impacts, to suggesting new service offerings or posting an alert to the management dashboard - to name a few. This means actionable information at decision makers' finger tips—and the comfort of knowing that it's based on an incredibly rich data warehouse and cross-checked in ways that weren't readily available a few years ago. What makes this really revolutionary are the algorithms that enable role-based views of the information that emerges from the systems.

Delivering Concise Results to Decision Makers

Asset managers have to interact with a wide variety of both internal and external stakeholders, including banks, investors, contractors, regulatory authorities, engineering personnel, and management—among others. All of these functions can benefit from role-based views of big data that have been distilled down to the essential information that each individual needs to make better decisions faster.

At Exelon, for example, Magin and his team aggregate and summarize prime mover and transmission system data to yield insights that result in recommendations for maintenance personnel. This approach focuses on power plant equipment reliability and forecasts the potential impacts of alternative maintenance plans.

Delivering results that are concise and targeted begins with the sensors and then products such as SmartSignal or NextNine. These types of predictive diagnostic software and services tool sets integrate everything from equipment operational parameters to powerful analytics, and include asset and system cyber security functions. These are digital solutions that analyze, in real time, industrial big data and create clear indicators of impending fault or failure.

Choosing a Streamlined Knowledge Dissemination Method

Power industry leaders have a lot riding on their decisions, as the stakes in terms of their plant's finances or potential collateral damage are often high. Thus, finding the communication channel that facilitates the fastest end result is vital to effective plant operation. As is the case for any good digital enterprise system, the fewer clicks that are needed to get to the end result, the better.

End users, such as the executive management team, have completely different needs compared to maintenance or finance personnel. Therefore, the information channels can range from smart phones to email, websites, and regular reports. Getting the biggest bang for the IIoT investment, however, should involve a serious look at knowledge dissemination methods. Information needs to be delivered in a form that decision makers can easily digest, without any superfluous data, and using a delivery channel that's most convenient for them.

Increasingly, success stories are emerging from the adoption of big data in power plants. A nuclear power plant achieved a 10 percent decrease in lost megawatt-hours—valued at $5 million. A wind farm increased electricity generation by 20 percent. Beyond equipment reliability and performance, though, big data is informing alternative actions for power plant operations and better defining financial risks and rewards.

The ultimate end-use customers, such as factories, businesses, and homes, are poised to gain the most from the utilization of big data in the energy industry, as it stands to increase electric power reliability and lower costs. In order to realize the full potential of the technology, however, power plants need systems in place to refine and analyze the massive amount of sensor data to create role-based views of the relevant information. In the age of the industrial internet, collecting data can be surprisingly simpler than communicating it to the various stakeholders across the enterprise.

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