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GE Power Digital Solutions
Using Advanced Analytics and Controls to Drive
Economic Value in a Complex Operating Environment
GE Power Digital Solutions
William J. Howard
A number of industries have been transformed by the wave of digital
innovations, big data, analytics and computing; more recently, the power
industry has begun to selectively apply these digital technologies to drive better
economic outcomes for existing and greenfield power plants. These disruptive technologies are arriving at a time when the power industry is encountering dramatic market dynamics resulting from changes in fuel prices, increases in renewables coming on line, and changes in the regulatory environment.
The challenges of managing power generation plants have become more complex—complicated system interactions, more co-optimization demands, and more operating profile flexibility. This convergence offers power producers an opportunity to embrace technology to re-position the competitiveness of their plant operations.
This paper demonstrates how today’s power operators can use a modern ecosystem to provide on-going operational productivity—protecting against downside risk while constantly pursuing upside opportunities and increasing economic value while reducing total cost. The paper will cover a brief history of big data and applied analytics usage in power plants today, as well as the dynamics in the industry that have created new operational complexities. We present a maturity model that provides a roadmap for end users to advance through different stages of applied analytics to drive incremental and sustained productivity. We then discuss the obstacles to implementing this maturity model while specifically recommending how to progress to prescriptive analytics within the Industrial Internet architecture, moving from business applications in the cloud down to the controls layer.
Industry dynamics In recent years, a variety of external forces have significantly impacted the way power generation companies run their assets and meet business objectives. An increase in the supply of renewables, combined with varying gas prices and a constantly changing regulatory environment, has increased the need for reliable, flexible power across generation assets.
Notably, as the appetite for renewable energies increases, traditional baseload units must shift to a more cyclic operating profile. This, in turn, introduces new patterns of wear and tear that ultimately drives new maintenance needs and behaviors and impacts reliability. The complexity of this dynamic is compounded by the fact that operators must balance pressures to reduce maintenance budgets, as shorter run times have resulted in substantially lower profits overall.
Using Advanced Analytics and Controls to Drive Economic Value in a Complex Operating Environment Additionally, the workforce managing today’s power plants is changing, as nearly 30% of today’s utility workforce is expected to retire in the next 5 years. The Bureau of Labor Statistics projects that the employment of power plant operators in nonnuclear power plants will decline by 11% from 2012 to 2022, a dynamic similar globally, which creates an environment where a smaller number of less experienced operators are tasked with operating power plants reliably. In general, the workforce is being replaced by a younger, more technologically-savvy employee who expects cutting-edge technologies similar to those found in the consumer space, which can make it hard for power companies with traditional legacy systems to attract good talent.
Industry Response Power producers have turned to data and analytics as a way to manage these dynamics. The use of data is not new to the industry, as operators have collected and stored machine sensor data in historians for years now. Many countries face regulations that require customers to keep a minimum amount of data for a set period of time. In fact, across the power generation space, data storage is expected to have a 30% compound annual growth rate from 2014 to 20201. Typically this data serves as the basis for post-issue resolution—when a machine breaks, a technician will leverage the data in determining a root cause and associated corrective action. A subset of customers have gone a step further, moving beyond basic data collection to creating simple dashboards that can highlight trends and deviations from the norm. In addition, some operators use condition-based monitoring (CBM) systems—i.e., vibration detection systems—as a means of detecting equipment issues before catastrophic failures occur.
More and more operators are considering ways to leverage data and analytics at greater scale; this includes collecting a larger number of data points, centralizing the data repository, purchasing analytic packages or additional CBM systems.
More advanced operators are going a step further, looking to transform their organizations by building out centralized monitoring and diagnostic centers and the associated engineering teams. Ultimately, use cases around data and
analytics can be described by a simple maturity model (Figure 1):
Figure 1: Maturity model Move from protecting downside to enabling upside
1 Based on Harbor Research and GE estimates, 2015.
© 2016 General Electric Company. All rights reserved. 3 As the application of analytics moves from descriptive to prescriptive, the nature of how to apply data and analytics changes from merely collecting information and doing basic trending to instead focusing on how to leverage data and analytics for true optimization. We see a shift in leveraging data to protect plants from financial downside (equipment failure leading to unavailability and expensive repairs) to enabling an upside (purposefully timed maintenance that balances operational risk and reward). As the analytics provide more valuable insights on the operational risks and opportunities, they need to be connected to both the people who make operational decisions and to the advanced controls that can adapt and maneuver the machines towards the desired outcomes.
Limitations Across Early Stages of the Maturity Model Though early stage data collection methodologies offer some benefits to plant operators, they still have significant process inefficiencies with little impact on key performance indicators (KPIs). For example, using trend charts for root cause analysis after the fact may reduce resolution time, but it does not contribute to improved reliability of the plant—a metric valued nearly universally. It also results in a reactive approach to maintenance, meaning operators engage in significant “firefighting” as issues emerge, which often incurs high maintenance costs and reduces availability. Periodic review of dashboards can help identify issues early but introduces its own set of complications, as interpretation is often subjective and time-consuming, particularly when coupled with a field workforce that is increasingly inexperienced. Potentially severe issues may go undetected, or alternatively operators can waste time chasing noncritical issues.
Over time operators have increasingly relied on CBM systems, and while these are effective ways to detect failures, they often do so within equipment siloes and lack capabilities to enable cross-business collaboration for speedier and more accurate issue resolution. This approach also tends to reinforce a need for system specialization and extensive training at a time when workforces are getting leaner.
Challenges Moving Up the Maturity Model More advanced operators will engage their IT departments as a way to help synthesize data across different point solutions.
This enables them to start moving away from merely protecting the downside to thinking through ways to optimize the upside. By engaging with IT, these operators hope to create an ecosystem—a connection of data, networks, computers, CBM systems, and the people who use them.
Despite the best of intentions, most of these efforts fall short. Usually the connections built are simple in nature—data going one direction and aggregated in a way that makes it difficult to ultimately view the underlying parameters. For example, most data integration software can highlight the total number of events, but an operator will be unable to drill into the details that triggered the alarm in the first place, requiring a manual piecing together of the data from disparate systems to see the full picture. This adds significant time to the resolution process, complicated by the fact that most people in the organization do not have access to all the systems, which makes it difficult or impossible to connect the dots. In addition, the effort to connect systems together introduces a significant cost and level of complication related to both systems integration and data storage.
Where analytical insights point to an opportunity to adapt a machine’s operating profile, significant delays to implement these improvements can occur unless the systems are connected through compatible software models. This inability to take analytical insights and make control changes that take full advantage of the machine’s operating envelope presents yet another obstacle to achieving operational excellence.
Using Advanced Analytics and Controls to Drive Economic Value in a Complex Operating Environment A New Paradigm: The Digital Ecosystem To continue to create new levels of business value in today’s environment, operators and critical power equipment suppliers must together think and perform in a new way—incorporating people and technology seamlessly into their business processes. Doing this requires a new infrastructure, one that is purposefully designed to enable continuous improvement while leveraging data from multiple sources, enabling more effective decision-making throughout the organization. The new digital ecosystem is designed to enable faster iterative learnings and optimization actions, an enhanced user experience across the operational team, and a new class of applications focused on targeted outcomes to create new business value.
Industrial-Specific Components. While several cloud platforms exist today, none has successfully combined key elements needed for developers to build applications geared towards the industrial space. The industrial environment introduces different-in-kind infrastructure needs: data services, asset models, and analytic services.
In most industrial settings, the thousands of sensors on hundreds of components and pieces of equipment generate data at sub-second rates, meaning that any infrastructure must have the ability to handle massive amounts of time series data.
Beyond time series data, the infrastructure should enable the collection of and quick access to a variety of data sources and data types, including work order history, weather data, drawings, etc.
© 2016 General Electric Company. All rights reserved. 5 Second, an industrial setting needs an asset model that can integrate this data. A typical relational database model cannot efficiently handle the complexity of relationships between the different data sources, requiring a graph database model instead. A graph database allows for greater flexibility as far as analyzing the interconnections between various data points, a typical feature of today’s complex business models.
Finally, the industrial setting requires a system that can take advantage of the new asset models and data services, allowing for a new set of advanced analytics. Analytic services provide operators with the ability to test and deploy analytics rapidly across a fleet of assets, and then monitor, improve and update the analytics as needed. This iterative dynamic is a key component that enables the development and implementation of prescriptive analytics.
Plant-Level Digital Infrastructure. At an individual plant level, the local controls architecture can dramatically increase the value of advanced software models and analytics. By connecting the insights of advanced analytics to the ways in which a machine can be optimally adapted to meet a new mission objective, the controls architecture creates flexibility in the operating envelope of power plants while configuring the system throughout the lifecycle so that plants remain relevant given changing industry dynamics. This relies on both physics-based domain knowledge as well as terabytes of operational and test data, enabling plants to migrate from traditional schedule-based control schemes to system integrated modelbased controls.
This advanced software modeling gives assets the flexibility to operate in a broader space, bounded by critical KPIs (e.g., output, emissions, life, ramp rate) as defined by the specific power producer at a specific point in time. In the case of complex co-optimization problems, dynamics such as trading life, heat rate and emissions can be studied through predictive simulations and then be directly implemented in the adaptive control software structure. Bridging cloud analytics, decision support, and adaptive controls gives operators the ability to consume big data from the plant and fleet to drive iterative improvements that can be quickly applied to provide better outcomes for both plant systems and individual machines.
Ultimately, the greatest benefit stems from controls and data analytics maturity increasing in tandem—specifically, to the point at which prescriptive analytics enable an asset to respond dynamically to allow power producers to reach critical KPIs on a given day, week, month or year.
Figure 3: Plant-Level Digital Infrastructure Secure, scalable, open and distributed
Using Advanced Analytics and Controls to Drive Economic Value in a Complex Operating Environment Putting the Ecosystem into Action With the industrial infrastructure in place, operators now have the capabilities to move up the data analytic and control maturity curve to execute on the above outlined benefits of the new ecosystem: 1) iterate quickly on advanced analytics;
2) utilize an expanded operational envelope; 3) develop and make use of intuitive applications; and 4) incorporate (1), (2) and (3) into business processes and operational decisions to create additive sources of value.