Attend this one-of-a-kind event to develop your digital roadmap, benchmark where you are compared to the rest of the industry and get a better understanding of all the technology available right now.

Dorothy Lozowski, Editorial Director of Chemical Engineering magazine, said, “For the fifth year in a row, Chemical Engineering will co-host this digital transformation event. Attendees will learn how others are adopting the latest digital technologies for process, product and enterprise improvements. We are thrilled that our industry colleagues Eastman Chemical are supporting the Connected Plant Conference, showing their support of peer-to-peer networking and advancement of digital technology solutions."

The Advisory Board and editors of Chemical Engineering and POWER magazines are hard at work to bring vital content to this year's Connected Plant Conference. From the dynamic world of artificial intelligence and machine learning to the enabling technologies driving advanced connectivity and data analytics, leaders will come away with tools to increase efficiency, productivity, reliability, and resiliency.

Attendees will also have the opportunity to meet with leading digital solution providers in the networking arena to discuss specific plant challenges and the digital technologies available to solve them.

Make plans today for your team and you to join Eastman Chemical at the Connected Plant Conference in Austin, August 30 – September 1, 2021. Registration is open, so visit our website today to register and receive the lowest rate available.

About Chemical Engineering magazine
Chemical Engineering magazine was launched in 1902 and is the most widely respected global information source for the chemical process industries (CPI). Chemical Engineering has been the leading source for news, technology and analysis used by engineers, operators, plant managers, senior managers and consultants worldwide. This combination of technology, analysis and experience makes Chemical Engineering the primary publication for the most important and influential people in the industry.

About POWER magazine
POWER, the single global resource for print, media, and events in the power and energy industry, was established in 1882 and is the only industry publication that addresses all power generation, and related technologies and fuels, utilized throughout the world, providing news and information for this increasingly complex sector. The POWER brand is dedicated to providing its global audience with exclusive analyses of the latest trends, best practices, and insight on power generation and related projects through several platforms, including print, digital, and in-person events. POWER equips generation professionals and those who support them with the resources they need to make informed decisions that power the future.

About Access Intelligence
Access Intelligence is a privately held b-to-b media and information company headquartered in Rockville, MD, serving the marketing, media, PR, cable, healthcare management, defense, energy, infrastructure, engineering, satellite and aviation markets. Leading brands include AdExchanger, AdMonsters, Chemical Engineering, Cynopsis, Cablefax, Chief Marketer, Defense Daily, Event Marketer, LeadsCon, POWER, Via Satellite and P3C. Market-leading conferences and trade shows include LeadsCon, AdMonsters OPS and Publisher Summits, Experiential Marketing Summit, SATELLITE Conference and Exhibition, OR Manager Conference, LDC Gas Forums, Clean Gulf, Experience POWER and The P3C Conference & Expo.

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Renewable energy resources, coupled with internet technologies and emerging large-scale energy storage, fundamentally change the modern electricity markets. Wind and photovoltaic electricity resources have proved to be the most disruptive renewable energy technologies over the past few years and will continue to do so. The challenge for grid operators is managing these disparate electricity providers to match weather-dependent supply with consumers demanding more independence and flexibility to manage personal demand patterns. The solution is multi-layered. A new regulatory framework is needed to address accelerating market changes, and electricity generators must evolve their business models to remain competitive. Technology developers must provide regulators and generators with the necessary computer tools to ensure a reliable and efficient electricity supply system continues.

Not all electricity suppliers are equipped to compete in this new electricity marketplace, particularly those with significant coal-fired infrastructure. Initially designed to operate at baseload for months on end, coal plants are often disadvantaged in a competitive electricity market that values flexibility in plant operations, particularly in those regions with sizeable amounts of renewable resources. Cycling most coal plants adversely affect equipment life, the startup is time-consuming, and operation at load low for an extended period of continuous load-following operation is challenging. Gas-fired plants, principally combined cycle plants (CCPs), feel the competitive heat from increased grid-connected renewable energy resources placed higher in the dispatch order. The common need by conventional steam plants and more modern CCPs is a plant control system designed for more efficient and flexible operation.

What does "flexible operation" mean to the power plant owner? As with any competitive market, the plant that delivers electricity under any operating condition will be called upon more often than plants unable to respond to the modern grid's dynamic load change characteristics. For example, flexible plant operations are usually stated in terms of its minimum continuous operating load, ramp rates, and fast startup when grid imbalances necessitate, while remaining in compliance with regulatory requirements. If a plant, coal or gas, cannot perform these three functions, then that plant is at a significant competitive disadvantage. Thus, regional system operators force thermal power plants to run under new operating conditions, much closer to their constraints and outside their stable zones, ruled by their control systems (Figure 1).

The Control Instability Problem

Thermal power plants under automatic control, like many other industrial plants, can suffer instability problems, particularly when power generation has to meet changes in demand. The main reason is that current automatic control systems are designed with fixed parameters, while the process dynamics are time-varying.

Power plants have several advanced process control (APC) systems available in the market based on various control methodologies, such as fuzzy logic, neural networks, or model predictive control. Each applies their control actions through well-known proportional-integral-derivative (PID) controllers at the DCS level, which interfaces with the plant's actuators and sensors.

Each control methodology also shares a common paradigm: their parameters are set to control a model of the process, which were previously developed under defined operating conditions. However, when the time-varying process dynamics diverge from the model dynamics, the control system and the PID parameters become detuned, and the control performance can become unstable. When the control performance is erratic, flexible plant operations are limited, and dispatched hours are reduced.

Some facilities have turned to AI as a means to improve plant response. The drawback to AI is the lengthy period of "deep learning," including "big data" collection under all possible operating scenarios before an AI controller can function independently. However, many of these technologies have proven to be more hype than hope, and the power industry has tested and rejected many AI-enabled solutions. The "control problem" now becomes correctly identifying the new dynamic relationship between control system input-output variables at all possible operating states, not just those that are predictable. For a modern steam plant, this is an impossible task.

Self-tuning AI: The ADEX Controller

Many electricity generators that are frustrated with their poor performance in a competitive marketplace may be surprised to learn that their plants are mechanically capable of greater operating flexibility, only to find that their plant controls are the limiting factor. If plant computing power or capability is not the issue, expensive hardware retrofits are also not the answer.

ADEX is a novel control technology that runs parallel with the plant DCS, not in the cloud. ADEX auto-adjusts its parameters in real-time to any of the infinite possible operating contexts and can guide process variables through desired trajectories, with no big data or machine learning systems. All this power relies on a new multivariable ADEX adaptive predictive controller that expands the stable process zone of operation (Figure 2) to include new challenging optimal scenarios.

An overview of how the ADEX controller accomplishes this impressive task is shown in the ADEX controller block diagram (Figure 3).

The methodological principles that govern ADEX controller operation and performance may be described in three parts.

1. Optimal Process Guidance describes the forward path of the ADEX controller block diagram that determines the controller operation (Figure 3). First, the process control signal is calculated by an adaptive predictive (AP) model to make the predicted process output equal to the desired output. Second, this desired output belongs to a future desired output trajectory produced by a Driver Block that goes from the actual process output to the setpoint. Finally, the Driver Block optimizes a process performance criterion that determines Optimal Process Guidance when there are no prediction errors.

2. Self-Tuning Artificial Intelligence (ST AI) is provided by an adaptive mechanism shown in the lower feedback path of Figure 3. From the process input-output data and the prediction error, it adjusts in real time the AP model parameters so that the square of the prediction error tends towards zero in the gradient direction. Thus, the process output trajectory will converge towards the desired output trajectory resulting in process stability and optimized control performance.

3. Expert Control is represented by the Expert Block on top of Figure 3. An ADEX controller is configured for different process operation domains based on the operator knowledge of process dynamics in these domains. The Expert Block introduces rules to make the controller configuration always the appropriate one for the current process domain of operation. Thus, prior operator knowledge on the process dynamics improves the closed-loop performance, robustness, and stability.

In sum, the ADEX implementation of AI based on this model does not require predefined operating points before it is implemented. It can adjust key operational variables by itself in real time to accommodate the application's time-varying context.

Improving Plant Operating Flexibility

The ADEX ST AI system identifies in real time the time-varying process dynamics, which enables the process output variables under control to be on target within an extended operational stable zone, where the thermodynamical, mechanical, and other design features of the plant enable greater operating flexibility. The mismatch of current fixed-parameter control systems and PID controllers with the time-varying plant dynamics can cause instability, such as oscillations of the process output variables, which reduces the operational stable zone of the plant and restricts flexible operation that could be achieved otherwise. Thus, the limiting factor for greater flexibility is not the plant's design capabilities, but the existing control system.

ADEX does not require replacing a plant's control system, the addition of new I&C, nor "big data" or long-term machine learning processes. ADEX obtains its data through the plant local area network (LAN) and control network plus minimal DCS reprogramming. ADEX can be turned on or off by the control room operators at any time. Using the same inputs as the existing plant control system, the ADEX Controller determines a different behavior on the actuator that brings a different process performance and different results.

Figure 4 illustrates the ADEX controller's performance when installed on the superheat control system of a conventional steam plant. The superheat (SH) steam temperature (green) is shown with its setpoint (red) during two hours of variable load operation. The left-side graph illustrates the existing APC's performance when controlling the SH spray valve position (orange). The lack of anticipation and the PID reaction to the error allows the process to oscillate, with relevant peaks and drops of temperature. The right-side graph illustrates the performance of the ADEX system's adaptive predictive technology when switched on. Note that a different behavior of the valve brings the SH steam temperature stabilization, even at minimum load. The following two case studies illustrate the application of the ADEX ST AI.

Case Study 1: Adding Operating Flexibility to a Coal-fired Power Plant

An ENEL three-unit 1.1-GW lignite-burning plant was struggling to deliver the load-following performance required by the system operator. Based on PID controllers, the existing control system produced unacceptable steam pressure and temperature oscillations, preventing the plant from accurately following the system operator's required load profile (Figure 5). The top graph shows how the existing APC was limiting the ramp rate to 1.5 MW/minute due to the oscillations in SH steam pressure (grey) with a maximum amplitude of 237 psi (1.650 kPa) and SH steam temperature (green) of up to 30F (16.6C).

After ADEX ST AI was applied, the ramp rate doubled to 3 MW/minute as shown in the bottom graph of Figure 5. The load delivered (in black) followed dispatch orders (in red) 73% more accurately than before. Steam pressure and temperature excursions were eliminated, increasing the stability of both processes by 45% and 47%, respectively. Thermal stress on the steam generator was significantly reduced, and its capability to cycle was enhanced. The time from minimum load to full load on the plant was halved from 3.5 hours to 1 hour 45 minutes. This performance improvement allowed the plant to be dispatched more often in a load-following mode.

Case Study 2: Boosting the Efficiency of a Combined Cycle Plant

The 786-MW Amorebieta 2×1 CCP, located in the north of Spain, is owned by Bizkaia Energia. The plant uses two General Electric MS9001 GA DLN 2.0+ combustion turbines (CTs) and an Alstom DKYZZ3-2N41 steam turbine. The plant operates in automatic generation control (AGC) mode, cycling more than 30% of load an average of 150 times per day. However, ramp rates and plant minimum load were limited by SH temperature excursions and subjected the plant to excessive thermal stress. The left-hand side of Figure 6 shows the plant has experienced peaks of +7C (+12.6F) and temperature drops of up to –30C (–54F) in SH steam temperature during cycling operation.

Bizkaia Energia developed a game plan to enhance the plant's dispatchability by increasing the plant's ramp rate and reducing its minimum load capability by successfully controlling the SH temperature oscillations. The right-hand side of Figure 6 shows how, since ADEX Self-tuning AI was applied, SH temperatures have maintained ±1.5C (±2.7F) around the setpoint 100% of the time, even under constant cycling operation.

With steam temperature excursions eliminated, the average steam temperature at the turbine inlet was raised by 4.8C (8.64F), which resulted in a heat rate improvement of 0.12%. The stability provided by ADEX optimizers also allowed an increase in the steam temperature setpoint of 2.1C (3.78F) for an additional heat rate improvement of 0.05%. Further, close control of SH temperature has reduced the induced thermal stress on the plant's superheater headers, increasing component life expectancy and plant availability.

Additionally, the plant has enhanced its dispatchability by increasing its ramp rate from 15 MW/minute to 24 MW/minute (a 60% improvement), and reducing minimum plant load from 270 MW to 205 MW (24%) in 1×1 operation. Ramp rate improvement will also be reflected in the plant's increased dispatch rate, particularly under AGC. The left-hand side of Figure 7 illustrates superheated steam temperature excursions that occurred during load changes under the previous plant control system operation. The right-hand side graph shows the operation of the plant after the ADEX optimizer was installed. The result was an increase in plant capacity delivered under AGC with SH temperature under close control.

Those higher ramp rates and lower minimum loads produced more demanding combustion conditions in the CTs too, affecting flame stability, NO_x formation, and combustion dynamics. The ADEX Self-tuning AI enabled the solution of those problems at Bizkaia Energia through the optimization of the fuel gas heaters, controlling the gas temperature five times more accurately than previously, following the Wobbe Index setpoint and meeting the demanding performance conditions imposed by flexible plant operations.

Conclusions

The ADEX Self-tuning AI allows under-performing plants to improve their thermal efficiency while minimizing thermal stress to the equipment, reduce startup time, and improve operating flexibility for both coal-fired and combined cycle plants. Flexible plants, particularly responsive plants with excellent ramp rates, high turn-down, and good load-following capabilities under AGC, are particularly valuable to a system operator. Also, the plant moves up in the dispatch order, which produces a greater economic return on the owner's plant investment.

–Jose Martinez is CEO of ADEX Group, Isaias Martin-Hoyo is COO of ADEX USA, and Ravi Krishnan is managing director of Krishnan & Associates. This article was written for ADEX in cooperation with Krishnan & Associates, a specialized energy industry marketing firm.

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One area where this is particularly a challenge is our power grid, which is increasingly threatened by changing weather patterns and has become more strained over time to meet the needs of growing populations with new consumption patterns. A lack of efficient inspection capabilities and other outdated asset management techniques means that new risks arise across the grid–from substations to customers. Even the power lines themselves pose a risk, as the right mix of dry and windy conditions can quite literally spark fires.

This is top of mind for utilities, as the industry has witnessed spikes in company-imposed blackouts in wildfire-prone areas like California. While this practice has proven to be effective, it causes significant headaches to customers and is costly to conduct, leaving many to desire a more efficient way to manage the grid during these events.

Data Can Help Prevent Disaster

One way to limit the need for public safety power shutoffs is through the effective use of data. For example, utility companies often rely on manual or in-person line inspections, but given the sheer scale of activity across thousands of miles of utility lines and distribution substations, it can be tedious and challenging to conduct thorough examinations. In recent years, the industry has turned to technology for help.

This is where integrating Internet of Things (IoT) sensors, imaging, light detection and ranging (LiDAR), weather predictions, and other technologies into the grid helps operators identify disturbances or anomalies, analyze activity in the grid, and even receive real-time alerts regarding line interference. This is critical to stopping potential sparks or flames before they start. As this data is collected over time, the effective use of the information can reduce the risk of wildfires through the ability to take action when and where it's needed–quickly and even preemptively.

However, these sensors, robots, and other monitoring tools generate mountains of data. While it can better inform future decisions, the scale and quality of data generated can be challenging to manage and effectively glean insights from. Thus, operators must look for ways to "liberate" this data from its silos in order to power data-driven insights that mitigate risk and protect employees, as well as consumers.

Liberate Data to Mitigate Risk and Boost Efficiency

The abundance of data at the disposal of utility companies and most organizations nowadays is unprecedented. The collection and storage of data has never been cheaper, but to get the maximum value out of data, and operate in a safer and smarter way, operators must be able to analyze and gather insights from the data before an impactful event occurs.

While gleaning insights from data may seem easy, it can be a major challenge when uncontextualized. That is why operators have struggled to this day, and also why utility companies and other heavy-asset industries are investing in data management. Through these investments, utility companies are working to make their organizational data more readily available and accessible to stakeholders. This allows all the data stemming from a utility company's operation to be processed in milliseconds, meaning decisions can be made faster, with strong reasoning based on data, which is critical amidst a natural disaster such as a wildfire where every second counts and a misstep can result in catastrophe.

The liberation and contextualization of data to mitigate the risk of natural disasters is just one use case for utility companies. For example, large grid operators can use data to monitor the condition of distributed transformer networks. With data-driven insights, grid experts and employees can proactively identify risks (without manual inspections) and schedule maintenance–decreasing the likelihood of minor failures, as well as major failures.

The Technology Is Ready and Waiting

Humanity's continued fight against Mother Nature has gone on for millennia, but we are no longer completely powerless in mitigating the impact and destruction of wildfires and other natural disasters. The proper use of data is our best defense.

Collecting it is no longer enough, power companies must proactively fuse their data, making it easily and quickly available to all stakeholders. This technology exists today, and is already being implemented by leading U.S. operators and other forward-looking companies around the world. To not only survive the energy transition, but also the effects of climate change on operations, data must be put to work.

–Francois Laborie, PhD is president of Cognite North America.

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The report, which analyzed more than 2,000 activities across 800 occupations, found that hybrid models of remote work will accelerate and continue after the COVID-19 pandemic ends. Remote work will primarily be held by a highly skilled, highly educated workforce in a few key industries. Within the utilities sector, an estimated 31% to 37% of employees have the effective potential to work remotely without diminishing productivity.

While the pandemic quickly unveiled the benefits of remote work, companies face ongoing challenges of workforce configuration, remote coaching, and collaborating from a distance. To be successful, these new models of work will require effective digital communication and collaboration at a distance. One element that offers space for significant improvement in productivity is how employees plan and conduct online meetings. In the 2020 State of Online Meetings Report by consulting firm Interaction Associates, nearly 46% of employees reported their online meetings are seldom or never effective. Unproductive meetings are often caused by unclear processes, poor attendance or engagement, and lack of explicit agreement building.

Today, energy sector companies have an opportunity to optimize employees' time, maintain productivity at a distance, and develop cultures where hybrid models can work. The prize? A boost in outcomes and employee retention, especially among millennials. This is especially important in the energy sector where 25% of workers are eligible to retire within the next five years and 50% are eligible in the next 10 years. To make hybrid arrangements work, here are three levers to pull to begin to improve productivity in online meetings.

Define Clear Meeting Objectives

For many employees, scheduling and attending online meetings is often done without detailed planning and purpose. Standing meetings to "review progress" or "connect" are common as remote employees don't have the advantage of being in the same room. While the technology to support online meetings is widely used (78% of respondents indicated their company meetings were conducted via an online meeting platform), many employees are experiencing meeting overload. Without clear guidelines and planning, meeting invitations can quickly appear on calendars and drag down productivity. To get focused, ensure that every meeting has a defined purpose. Writing "Desired Outcome Statements" is a simple but powerful way to answer the question, "What will we leave this meeting with?".

Clarify Roles and Participation

Hybrid work models can be attractive due to reduced operational costs, higher employee productivity, and a greater selection of job candidates. However, remote work can also be frustrating if there is a lack of guidelines for how people are involved in conversations, decisions, and meetings. It's easy for meeting attendance to quickly swell in group size. To ensure there is high engagement and participation, the meeting leader should ensure that clear meeting roles exist (Figure 1). If necessary, assign others to take on a role (such as scribe or timekeeper).

As affirmed in Incident Prevention, safety within utility operations is especially critical, and poor decisions are often formed by employee habits, which can quickly lead to errors.Similar to preventing safety incidents, clear procedures and employee habits in clarifying roles within meetings can boost communication and collaboration.

Capture and Articulate Action

In the 2020 State of Online Meetings Report, one key element stood out in translating conversation into results and achievement: having someone in the meeting capture key ideas and action items on a shared screen, and giving everyone access to the notes after the meeting.

If someone captured key ideas half the time, only 49% of respondents left the meeting with a clear understanding of decisions and action items. For those situations where key ideas were always captured, 89% of respondents left the meeting with clarity on next-step actions. If the scribe shared the key ideas with the group after the meeting, 92% of people completed action items on time. For those meetings where notes were seldom or never shared, only 35% of people completed action items.Capturing specifics and articulating desired actions moves work forward.

In summary, online meetings will continue to be a major component of how employees connect, deliberate, plan projects, and drive accountability. Implementing a skilled meeting culture where employees communicate clear meeting objectives, roles, and capture key ideas and decisions helps to propel initiatives–and companies–forward.

–Chris Williams is the director of Business Operations for Interaction Associates, serving the top companies in the energy industry. Interaction Associates is best known for introducing the concept and practice of group facilitation to the business world in the early 1970s. For more than 50 years, IA has provided thousands of leaders and teams with practical, simple, and effective programs, tools, and techniques for leading, meeting, and working better across functions, viewpoints, and geographies.

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"This is just by far the hottest discussion point at the senior management or executive level at energy companies, as well as chemical companies. They're investing as fast as possible to become ‘sustainable.' There are several reasons, but the biggest is investment pressure and stock price pressure," said Ron Beck, market strategy director at Aspen Technology Inc., a provider of enterprise asset performance management, monitoring, and optimization solutions.

AspenTech recently commissioned a study on sustainability, which was conducted by ARC Advisory Group. The analysts surveyed more than 200 energy and chemical industry professionals from around the world and found that 90% of their companies have sustainability initiatives in place. The researchers noted, in general, energy companies tended to focus on the transition to a lower-carbon future, while chemical companies often place more emphasis on producing sustainable products. Regardless of the objective, however, Beck said improving energy efficiency can have the biggest impact on most companies' sustainability metrics. "If you reduce your energy use, you increase your profitability," he said. "It's a net-net positive."

Somewhat surprisingly, when ranking sustainability goals, survey respondents said improving operational safety was their top priority, above such things as implementing net carbon reductions, reducing emissions, adopting cleaner energy sources, and reducing waste and water use. The report says, "Process safety incidents impact environment, personnel, brand, profitability, and the ability to grow a company." The study suggests leaders know very well that one major safety incident can limit a company's license to operate, and therefore, they consider safety of utmost importance to sustainability.

Another interesting finding from the study was that 75% of respondents said digital transformation is extremely important or very important for achieving sustainability goals. ARC's analysts said digital technology that "augments people and knowledge" is already actively being used "to improve both business and environmental performance and sustainability."

"One of the objectives of digitalization is to measure, and measure in real time," Beck told POWER. "Instead of taking all year to put together your annual sustainability report, if you actually want to take actions to avoid unpleasant surprises at the end of the year, executives need the data now. ‘Why is this plant performing worse than that one? What is it doing wrong? Let's investigate or let's send the team that's doing the best to teach everybody else what they're doing.' I mean, it's sometimes as simple as that," he said.

So, what's holding back sustainability initiatives? The majority of survey respondents identified a "lack of capital or resources" and "aging assets" as top barriers to meeting sustainability goals. The report says: "While most companies have a chief financial officer, chief legal officer, and other traditional leadership roles; there is not usually a chief sustainability officer. Since sustainability decisions are made by whoever controls the asset, those decisions are often made without considering the secondary effects of climate emissions. Furthermore, even at those companies with sustainability leadership, there is often a disconnect between the sustainability leadership and operational roles such as plant operations, engineering, or supply chain."

ARC recommended three actions companies could take to more effectively meet their sustainability objectives. First, rethink short-term goals in light of COVID-19 disruptions. However, it said leaders must not let the pandemic distract them from longer-term business and sustainability objectives. Secondly, make targeted investments in supply chain and process optimization technologies, as well as in predictive and prescriptive analytics. Lastly, invest in people and develop a culture of waste elimination, with the ultimate goal of efficiently delivering environmentally friendly products safely. That's were true sustainability resides.

–Aaron Larson is POWER's executive editor.

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DURHAM, N.C., Feb. 8, 2021 /PRNewswire/ — Bayshore Networks and GE Digital today announced an expansion to their partnership to integrate their solutions to address the growing need to secure industrial and critical infrastructure networks. GE Digital’s OpShield technology will be integrated into Bayshore Networks’ advanced solutions providing sophisticated industrial cybersecurity and active prevention/protection for industrial equipment, including programmable logic controllers (PLCs), human machine interface (HMIs), and engineering workstations.

The post Bayshore Networks and GE Digital Expand Partnership to Secure Industrial and Critical Infrastructure Networks appeared first on IIOT Connection.

Many companies are involved in the research and development of technologies to support AI and ML. Beyond Limits, a California company launched in 2014, works in several markets, from energy–including the power generation and oil and gas exploration sectors–to manufacturing and industrial, as well as healthcare. The company says, “Our mission is to create automated solutions with human-like powers of reasoning that amplify the talents and capabilities of people. We specialize in complex challenges in extreme environments.”

Stephen Kwan, the company’s director of product management for Power Generation/Grid Management, provided POWER with his insight into AI and ML as it applies to the electricity sector, with a look at what the future holds as the power generation landscape continues to change.

POWER: How can the power generation industry benefit from AI and machine learning?

Kwan: Machine learning (ML) and advanced artificial intelligence (AI) solutions are valuable sources of technology currently powering the development of smart applications that have the ability to make very accurate decisions, autonomously, based on learned historical data. Such technologies are being used extensively by the power generation industry to develop customer-centric solutions that understand the evolution of their needs, leveraging essential data such as historical knowledge, expertise, and best practices to make automatic recommendations when decisions to challenging scenarios have to be made in real-time.

Globally, experienced power generation operators represent an ever-shrinking workforce whose deep domain know-how and expertise are critical to efficient, safe, and reliable operations. This subject matter expertise needs to be captured, digitized, and made accessible across the workforce in order to ensure the long-term continuity, efficiency, and reliability of operations across the power generation domain. Novel hybrid AI/ML approaches represent a necessary avenue for combining the value of data and domain knowledge to tackle global challenges facing the power generation industry.

POWER: What about the impact of AI and ML on distributed power generation?

Kwan: AI/ML approaches will add significant value to the ever-growing deployment of large-scale, geographically distributed energy resources. The optimal management of trade-offs between meeting demand, ensuring low risk and reliable operations, and maintaining the integrity of key assets will require AI/ML systems capable of building and supporting holistic models of specific power planning and scheduling, generation, and distribution processes.

Advanced AI/ML solutions can significantly improve the management of the integrity and health of key large-scale assets spanning the generation (e.g. turbines) and distribution (e.g. grids/smart meters, etc.) domains. This can be achieved by combining predictive models (both supervised–learning to recognize and predict specific events and patterns– and unsupervised–learning to detect anomalies and potential events of interest) that learn from a wealth of historical data on critical assets and their operations. Such systems are educated and informed by relevant domain expertise captured and digitized from skilled operators, engineers, and decision-makers.

Asset health and performance are constantly changing as time progresses. In order to maximize profits, it’s important to continually learn from, and adapt to, these changes. AI solutions can provide a dynamic assessment of key parameters, in combination with leveraged domain expertise, leading to more optimal operations of valuable power generation assets. Power generation facilities are often optimized to run within a specific operating envelope and with most operators being trained to support these scenarios. With the proliferation of various energy resources, such as solar and wind, operators must adapt to running such equipment independent of traditional operating ranges. AI can take into consideration the interactions of these unconventional operating set points–in combination with subject-matter expert expertise–to more effectively operate the assets optimally, with lower risk.

POWER: Can machines used in power generation learn from their experiences; would an example be that a machine could perform more efficiently over time based on past experience?

Kwan: Advanced artificial intelligence solutions have the ability to empower software applications to help the power generation industry better analyze large data sets and ID patterns. Such AI systems continually monitor entire processes and learn over time based on historical experiences and data, helping power generation facilities better detect anomalies and make more precise predictions, thus improving overall operational efficiency.

Advanced AI approaches have the potential to revolutionize the management of critical assets across the power generation domain, combining historical and real-time operational datasets with deep subject matter expertise to provide recommendations on asset health management to maximize the efficiency, reliability, and safety of operations while meeting power generation demand and financial objectives.

An example would be leveraging AI solutions to help minimize unexpected system trips and other risks to asset health, integrity, and longevity. Many variables and factors, both historical and real-time operations, such as planning and scheduling targets, equipment constraints, maintenance and more must be monitored holistically in order to remediate and mitigate potential issues in a timely manner, reducing risks to generated power output targets, personnel, assets, and reliability of operations.

AI/ML solutions, trained with and exposed to data from both normal operations, and–when applicable–circumstances representing anomalies or abnormal operations, can extract relevant patterns across historical and current data, predicting and detecting potential system trips and quantifying the risk of unexpected downtime. By means of digitizing relevant domain knowledge, such advanced solutions can then generate explainable and actionable recommendations to help mitigate and remediate potential risks identified by data-driven models targeting critical equipment and assets.

POWER: Are there specific challenges for power plant operators that AI and ML can help solve?

Kwan: A challenging scenario could be that a particular power generation facility may need to take drastic actions if another facility in the grid goes down. In such an instance, machine learning and other AI approaches can be used to predict a facility’s limit to ramp up in support of the generation facility experiencing loss. For example, if a power plant goes down due to a trip, the remaining generation facilities participating in the grid would need to make up the loss of many megawatt hours. In that instance, it’s critical to know the maximum each facility can produce to make up for the loss.

Machine learning and other AI approaches continually learn from historical operating parameters and conditions, decreasing the odds of faltering in the execution necessary to achieve specific targets and goals. This fact remains true under all operating conditions regardless of the skillset, experience, or knowledge base of any one operator. In addition, such solutions digitalize and streamline how essential knowledge is passed between operators thereby decreasing misinterpretation or miscommunication.

POWER: There are certainly many uses for AI and ML in the power industry; what are some of the major ways the technology can support operations?

Kwan: Artificial intelligence can advance entire operations in the power industry by improving infrastructure and asset monitoring, power trading, and outage supervision, prediction, and planning. AI systems can also help better forecast scenarios of various natures including load and power generation. Such unparalleled insights could yield greater potential for power trading and increased risk mitigation.

Outside of uptime optimization for power generation equipment and facilities (e.g. turbines, distributed energy resources, etc.), advanced AI solutions also have the capacity to digitalize and democratize domain knowledge and operational know-how captured from skilled workforces to help solve the challenge of a shrinking global workforce. Improved asset integrity management, predictive maintenance, and anomaly detection with recommendations for remediation/mitigation lead to maximization of operational efficiency against risk, reliability, demand planning, and other similar constraints. This is accomplished via optimized placement and selection of sensors distributed across critical assets to maximize the accuracy of forecasting and detecting operational anomalies.

The issue of distributed energy resources can also be impacted by artificial intelligence. Optimizing operations in a safe and reliable manner without negatively affecting asset health and longevity (e.g. faster start-up and shutdown of gas turbines) can be the basis for minimizing cost, maximizing profit, and ensuring stability for grid equipment health/lifespan. Advanced AI software can act as virtual sensors to infer what a measurement would be in the absence of a real sensor or unmeasurable property, thus providing support for better assessment and prediction of electric grid stability and potential brownouts or blackouts during urban planning processes.

POWER: How can the trend of decentralized power generation benefit from AI?

Kwan: The global energy market is transforming from highly centralized power distribution systems to more complex networks of decentralized systems and the industry is having trouble integrating distributed energy resources and renewables during this transitional period. Intelligent asset management strategies and notifications systems provided by AI solutions can increase grid reliability and provide better maintenance for an aging infrastructure (transmission and distribution systems).

The variability and uncertainty associated with decentralized systems further complicate the management of critical assets as opposed to smaller-scale, localized power generation facilities. AI/ML approaches will play a significant role in augmenting, monitoring, and managing capabilities across decentralized resources, leveraging a wealth of current and historical data streams while incorporating the deep industry know-how required to ensure the adoption and deployment of such AI-based systems.

Decentralized systems often operate in silos, affecting their ability to detect and account for perturbations that can be introduced by others. AI provides the basis to understand the effects of individual actions on the stability and reliability of the grid and overall generation capabilities.

POWER: How important is AI to smart grids? How important is AI to the integration of e-mobility (electric vehicles, etc.) to the grid?

Kwan: Artificial intelligence is very important to smart grids as they consist of smart meters/appliances, renewable and other energy-efficient resources, and a large number of other devices. These devices provide a gold mine of data that only intelligent systems can decipher and convert into valuable insights and forecasts. It is also extremely difficult to model those devices because it’s challenging to understand the effects of changes on the overall availability and reliability of the grid. Advanced AI solutions can provide the ability to model and understand changes that can affect the operations of the electric grid–such as the effect of increasing EV usage, for example.

POWER: How can AI be used in power trading, with regard to forecasts, etc.?

Kwan: Artificial intelligence solutions such as supervised learning models can predict the behavior of creditors or consumers with a high degree of accuracy. Algorithmic trading leverages reinforcement learning to reward and/or rebuke trading bots in response to how much money is made. Rules must be put in place such as stop-loss prevention, of course. This is comparable to a self-driving car security mechanism that would prevent pedestrian/car collisions. Advanced AI can reduce risks from current events and their inherent impacts on the market as models can be retrained with new market conditions front-of-mind. While human intervention and careful monitoring of deployed algorithms will still be required for the foreseeable future, the support of AI has the potential to help the industry ramp up technological advancements at a more accelerated pace.

The accuracy of electricity demands has a huge impact on power trading.  Inaccurate forecasts can lead to losses for the traders and (most importantly) affect power availability. For example, if forecasts are wrong and power generation units are sitting idle when power shortfalls exist in the grid, this can lead to very costly and disruptive mitigations. AI solutions can support complex forecasting models with a large number of inputs and simple updating capabilities to reflect dynamic changes that provide more accurate near real-time forecasts.

POWER: How important is AI to the design and "construction" of virtual power plants?

Kwan: A virtual power plant is a clustering of distributed heterogeneous generation units and an integral part of the “Internet of Energy” (improved monitoring and control of smart grid). Intelligent technological solutions are necessary to utilize before the plant construction phase to ensure the design meets the intended supply generation needs and for identification of potential problems, the projected location and cause of those problems, and probable actions for minimization of adverse impacts. Intelligent electronic devices are used to control the flow of power and operate equipment while making local decisions. AI solutions can enable the analysis and interpretation of “what-if” scenarios enabled by the design and construction of virtual power plants, enhancing learning capabilities that can ultimately be transferred to real-world operations in the form of predictive models and decision-making advisors. AI models can supplement first-principle based models when developing a virtual power plant – especially in cases where first-principle methods are too slow or too complex.

POWER: Machine learning and AI in power generation rely on digitalization. As the use of data becomes more important, what steps need to be taken to support AI and machine learning while still accounting for cybersecurity?

Kwan: Given AI requires a lot of data, it’s vital to incorporate subject matter experts when creating the machine learning/AI models so as to understand the minimum required data set. It’s also important to follow IT best practices to ensure data is isolated from critical infrastructure like the control network, confirm only the least-required privilege is given to the users or applications, and implement logging and audits per industry security measures.

In a typical power generation facility, the control system resides on an isolated and dedicated network. Meanwhile, business users are typically on separate networks that are more “open” for cybersecurity threats. All AI/ML applications require good/clean data for optimal functionality. As such, any data necessary for AI/ML solutions–that reside on the control network– need to be pushed to business networks in a safe and reliable manner. AI/ML applications should avoid reaching across isolated networks to access necessary data. This requires good IT practices, employing a solution to take advantage of products that permit only one-way data flow (e.g. data diodes).

By preventing network traffic into the control network and only allowing data to be pushed from the control network, it decreases the chance of cybersecurity incidents on the control network. Industry best practices for ensuring data integrity should be deployed to ensure data is not changed. If changes are necessary, they must be auditable. Lastly, any recommendations for the AI/ML application should be explainable so that subject matter experts can verify the results if/when appropriate.

POWER: What do you see as the future of AI and machine learning for power generation / utilities?

Kwan: Integration and adoption of machine learning and other artificial intelligence solutions are already increasing exponentially. At this pace, the outlook for advanced technologies points toward AI approaches becoming the gold standard for the future growth of the power generation/utilities industry.

–Darrell Proctor is associate editor for POWER (@POWERmagazine).

The post The POWER Interview: AI, Big Data, and Efficiency appeared first on IIOT Connection.

Waltham, MA – February 2, 2021 – Boston Dynamics, the global leader in mobile robotics, today announced an expanded product line for its agile mobile robot Spot. The new products include a self-charging Enterprise Spot, web-based remote operations software, Scout, and the Spot Arm. These additions extend Spot's ability to perform autonomous, remote inspections and data collection, and enable the robot to perform manual tasks.

With more than 400 Spots out in the world, the robot has successfully undertaken hazardous tasks in a variety of inhospitable environments such as nuclear plants, offshore oil fields, construction sites, and mines. Customers have leveraged Spot's advanced mobility, autonomy, control, and customizability to improve operational efficiency, enhance worker safety, and gather critical data. Spot's new products are designed to enable customers to fully operationalize continuous, autonomous data collection on remote or hazardous worksites of any size, from anywhere they have access to their network.

Autonomy is critical to enhancing Spot's value. In order to support long, remote deployments, Boston Dynamics is introducing Spot Enterprise, a new version of Spot that comes equipped with self-charging capabilities and a dock, allowing it to perform longer inspection tasks and data collection missions with little to no human interaction. In addition to the basic capabilities that the base Spot robot offers, Spot Enterprise leverages upgraded hardware for improved safety, communications, and behavior in remote environments. These upgrades expand the range that autonomous missions can cover, extend WiFi support, add flexibility to Spot's payload ports, and enable users to quickly offload large data sets collected during the robot's mission.

Pivotal to refining Spot's value at scale is remote operation. Scout is Boston Dynamics' web-based software that enables operators to control their fleet of Spots from a virtual control room. Operators can use Scout to take Spot anywhere a person could go on-site, allowing them to inspect critical equipment or hazardous areas from afar. The software is designed with a simple user interface to run pre-programmed autonomous missions or manually control the robot, to perform various tasks such as walking or posing the robot to capture images and thermal data of obscured gauges or pipes using the Spot CAM+IR thermal imaging payload.

Combined, the Spot Enterprise robot equipped with a Spot CAM+IR thermal imaging payload, Scout software, and Boston Dynamics' premium support now create an out-of-the-box solution for asset-intensive environments. Operators can deploy this solution on site to proactively maintain and manage assets while maximizing worker uptime and improving worker safety.

In addition to launching products designed to make remote inspection safer and easier, Boston Dynamics is also releasing the Spot Arm, which enables users to act on data insights and perform physical work in human-centric environments. The arm is equipped to operate through both semi-autonomous actions and telemanipulation. It can manually or semi-autonomously grasp, lift, carry, place, and drag a wide variety of objects. It is also capable of manipulating objects with constrained movement and can open and close valves, pull levers and turn handles and knobs in coordination with its body to open standard push and pull doors.

"Since first launching Spot, we have worked closely with our customers to identify how the robot could best support their mission critical applications," said Robert Playter, CEO of Boston Dynamics. "Our customers want reliable data collection in remote, hazardous, and dynamic worksites. We developed the new Spot products with these needs in mind, and with the goal of making it easy to regularly and remotely perform critical inspections, improving safety and operations."

Interested parties can purchase Spot Enterprise, Scout, and the Spot Arm via Boston Dynamics' sales team. For more information on these new offerings, please visit: www.bostondynamics.com.

About Boston Dynamics
Boston Dynamics is the global leader in developing and deploying highly mobile robots capable of tackling the toughest robotics challenges. Our core mission is to lead the creation and delivery of robots with advanced mobility, dexterity and intelligence that add value in unstructured or hard-to-traverse spaces and positively impact society. We create high-performance robots equipped with perception, navigation and intelligence by combining the principles of dynamic control and balance with sophisticated mechanical designs, cutting-edge electronics and next-generation software. We have three mobile robots in our portfolio – Spot®, Handle and Atlas® – as well as Pick, a computer vision-based robotics solution for logistics. Founded in 1992, Boston Dynamics spun out of the MIT Leg Lab and is one of Inc. Magazine's Best Workplaces of 2020. For more information on our company and its technologies, please visit www.bostondynamics.com.

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The increased digitization of the power generation industry means that artificial intelligence (AI) and machine learning (ML) are becoming synonymous with power generation. AI brings great potential to the future design of energy systems across several applications, including smart grids, electricity trading, efficiency, and the networking of power consumers and generators (Figure 1).

1. There are many uses for artificial intelligence (AI) in the energy industry, and for power generation in particular, including for smart grid applications. AI also can help with operations and maintenance, forecasting, and energy trading. Courtesy: Next Kraftwerke

ML includes a part, but not all, of AI. For power companies, ML provides the opportunity for machines to learn independently, drawing conclusions from their experiences for future operational efficiencies, with the machine solving problems, and reducing operations and maintenance downtime and costs.

Increasing the use of both AI and ML in the power generation sector means making wise use of a growing amount of available data, with a goal of making electricity production and use more efficient and secure. It also means developing ways to more quickly analyze and evaluate ever-larger volumes of data. AI offers many suitable application scenarios to support the integration of renewable energy resources to the power grid, as more utilities adjust their generation fleets to meet climate goals.

"AI is transforming several different industries," said Colin Parris, senior vice president and chief technology officer for GE Digital, in an interview with POWER, "[including] power generation, information, aviation, and transportation. Today, you have to know how to build a data system and build the analytics." Parris noted how GE uses the Lean Methodology, which is a way of optimizing the people, resources, effort, and energy of an organization to create value for customers. GE chief executive Larry Culp is a proponent of the system, and GE and other companies have incorporated it into their business practices.

"I grew up in a world where, whenever I did a digital transformation, I had to do a process transformation," said Parris. "Now, I can go and get all the KPIs [key performance indicators], all the data comes in one spot… for data scientists like me, all the data shows up, and Lean gives me all the business process information."

Balancing Baseload and Renewable Power

Having volumes of data available, particularly to evaluate the performance of equipment and systems, provides the opportunity to continually improve the production, transmission, and distribution of electricity. It's become even more important as more renewable energy resources come online.

The balancing act between baseload power, and the intermittent nature of renewables, means utilities and transmission system operators need ways to automate systems to account for fluctuating supply and demand. AI and ML will play an increasingly larger role.

"Promising new sources of renewable energy–solar and wind–are variable in nature. As such, power and wind energy outputs are rarely consistent and uniform, and they depend on a variety of outside factors–intensity of solar radiation, cloud cover, wind speed–that can't be controlled," said Dijam Panigrahi, co-founder of Grid Raster, a company that provides computing and network services to a variety of industries, including power generation. GridRaster can create a virtual environment close to real-world settings, and provide realistic product visualization and real-time collaboration, so an effective environment can be created for remote maintenance, repair, and training.

"Machine learning could revolutionize the way we deal with energy," Panigrahi told POWER. "Its impact ranges across the areas of renewable energy forecasting and smart grids. With ML, for example, we could accurately predict the amount of electricity generated by a wind turbine in the next 36 hours, and therefore, we would be able to effectively transition to renewable energy without destabilizing the power grid."

"The benefit [of AI and ML] is higher utilization of the existing infrastructure," said Luke Witmer, general manager for Data Science, Energy Storage, and Optimization at Wärtsilä. "There is a lot of under-utilized infrastructure in the power generation industry. This can be accomplished with greater intelligence on the edges of the network [out at each substation and at each independent generation facility] coupled with greater intelligence at the points of central dispatch."

Read this POWER Interview with Luke Witmer of Wärtsilä to learn more about how AI and ML impact the power generation sector.

Witmer told POWER that AI has a role in the design of virtual power plants, and can be used to model dispatch from other generation facilities. "Today, any power plant [virtual or actual], is designed through a process that involves a lot of modeling, or simulations of what-if scenarios," he said. "That model must be as accurate as possible, including the controls behavior of not only the new plant in question, but also the rest of the grid and/or markets nearby. As more AI is used in the actual context of this new potential power plant, the model must also contain a reflection of that same AI. No model is perfect, but as more AI gets used in the actual dispatch of power plants, more AI will be needed in the design and creation process for new power plants or aggregations of power generation equipment."

Long-Term Efficiency

The real-world applications of AI and ML in power generation are being recognized in a variety of ways, certainly in operations but also as part of workforce efficiencies.

"Globally, experienced power generation operators represent an ever-shrinking workforce whose deep domain know-how and expertise are critical to efficient, safe, and reliable operations," said Stephen Kwan, director of Product Management for Power Generation/Grid Management at Beyond Limits, an industrial and enterprise-grade AI technology company. Kwan told POWER, "This subject matter expertise needs to be captured, digitized, and made accessible across the workforce in order to ensure the long-term continuity, efficiency, and reliability of operations across the power generation domain. Novel hybrid AI/ML approaches represent a necessary avenue for combining the value of data and domain knowledge to tackle global challenges facing the power generation industry."

Beyond Limits in December 2020 announced it is working with NVIDIA to advance an initiative for bringing digital transformation to the energy sector. NVIDIA provides AI computing platforms; the company's graphics processing unit software serves several industries with virtualization solutions.

"AI has the potential to make a major impact on problems facing the heart of the global energy business, but the technology requires high levels of computing power to operate on the level and scale required by many of today's global producers," said AJ Abdallat, CEO of Beyond Limits. "With NVIDIA technology support and expertise, Beyond Limits is better positioned to offer faster, more intelligent and efficient AI-based solutions for maximizing energy production and profitability."

2. A cognitive power plant project in Africa will use artificial intelligence and technology developed by Beyond Limits to monitor the facility's operations. The technology originated with NASA. Courtesy: Beyond Limits

Beyond Limits in 2019 was awarded a contract by Switzerland-based Xcell, a global financial and minerals development company, to build the world's first cognitive power plant (Figure 2). The company said the plant "is part of a large-scale infrastructure program to drive core industrial capacity and power economic development in West Africa." The $25 million deal includes the development of Beyond Limit's Cognitive Power Generation Advisor, a technology which originated for NASA missions, to monitor operations at a natural gas-fired power plant.

Cybersecurity Concerns

As with anything in the digital realm, cybersecurity concerns are part of the landscape when dealing with increased digitization and the use of data.

"Even with a smart grid, there is a potential source of cyber concern," said Panigrahi. "A central system that collects data about the energy usage habits of millions of users can emerge as a target for malicious cyberattacks. This could potentially destabilize a grid while also damaging precious consumer data. Power companies must deploy comprehensive cyber defense systems through a regular cadence of perimeter tests in order to have a high level of confidence to protect the system. Additional technologies from augmented reality and virtual reality can aid and assist in these perimeter tests to spot and identify potential threats to the digital perimeter."

Said Kwan: "Given AI requires a lot of data, it's vital to incorporate subject matter experts when creating the machine learning/AI models so as to understand the minimum required data set. It's also important to follow IT best practices to ensure data is isolated from critical infrastructure like the control network, confirm only the least-required privilege is given to the users or applications, and implement logging and audits per industry security measures."

Kwan continued: "In a typical power generation facility, the control system resides on an isolated and dedicated network. Meanwhile, business users are typically on separate networks that are more ‘open' for cybersecurity threats. All AI/ML applications require good/clean data for optimal functionality. As such, any data necessary for AI/ML solutions–that reside on the control network–need to be pushed to business networks in a safe and reliable manner. By preventing network traffic into the control network and only allowing data to be pushed from the control network, it decreases the chance of cybersecurity incidents on the control network."

Kwan noted other aspects of generation related to AI and ML. "AI/ML approaches will add significant value to the ever-growing deployment of large-scale, geographically distributed energy resources," he said. "The optimal management of trade-offs between meeting demands, ensuring low-risk and reliable operations, and maintaining the integrity of key assets will require AI/ML systems capable of building and supporting holistic models of specific power planning and scheduling, generation, and distribution processes."

Kwan said the use of AI and ML applications in the power generation and utilities sector will only grow in the coming years. "Integration and adoption of machine learning and other artificial intelligence solutions are already increasing exponentially," he said. "At this pace, the outlook for advanced technologies points towards AI approaches becoming the gold standard for the future growth of the power generation/utilities industry."

–Darrell Proctor is associate editor for POWER (@POWERmagazine).

The post Using Data for Smarter Power Generation appeared first on IIOT Connection.

NRG Energy is a leading power generator and retailer with 35 generating facilities producing 23 GW of power to help meet the needs of more than 3.7 million residential, small business, and commercial and industrial customers served through a diverse portfolio of retail brands. NRG has committed to cutting its carbon emissions in half by 2030, even as the company expects to continue growing.

NRG recognizes the role technology will play in this transformation and how Industrial Internet of Things (IIoT) devices can help improve efficiency, and optimize how power is produced and consumed. In a blog post on the company's website, NRG shared its vision of what IIoT connectivity can provide, which is, "a real-time view of energy as it is generated and consumed, not just at a corporate level but at a utility and soon, perhaps, a grid level."

Several of NRG's generating plants are in Texas, which operates a competitive energy market through the Electric Reliability Council of Texas (ERCOT). ERCOT is the independent system operator (ISO) that controls about 90% of the electricity distribution in Texas. Unlike other ISO markets, which use longer-term forecasting, the prices in the ERCOT market are determined daily through an exchange, based on available capacity and demand.

The 3.7-GW W.A. Parish generating station, located about 25 miles southwest of Houston, is the largest power plant in the NRG fleet. These days, it needs to be available to respond to ever-changing load demand, because generating units designed to run at a steady baseload are now operating at variable loads, with some units even being stopped and restarted daily to meet demand at certain times of the year. Such flexible operation places new stresses on the equipment and makes failure detection more challenging. At W.A. Parish, IIoT sensors have been combined with process data to improve availability and reliability.

Continuous Improvement of the Asset Reliability Program

Equipment reliability at NRG means the difference between meeting its power generation commitment or purchasing from competitors on the open market. NRG has an existing well-developed predictive maintenance (PdM) program, which includes infrared thermography, oil analysis, and vibration analysis, but the company sought to supplement its monthly walkaround data collection with more frequent readings under differing load conditions.

To better assess equipment condition, additional data collection was required, so the NRG team decided to add continuous wireless asset monitoring that would provide more frequent data and allow earlier detection of developing failures. Going wireless was desirable because wiring the sensors into the plant's distributed control system (DCS) would have made the program cost prohibitive.

The PdM team also recognized the benefit of combining asset condition data with historical process data from its OSIsoft PI System. The combination of the two systems was useful to both the PdM team and process engineering. The PdM team wanted to use process data to have a more comprehensive ability to detect failures. The process engineering team wanted to use the data to improve plant performance by understanding the impact of process variability on asset condition.

With their mutual interests in mind, the two groups decided to form a multi-disciplinary team to define the requirements and establish goals for the usage of continuous asset condition data. The requirements that they defined were:

■ Cost effectiveness–wired systems were cost-prohibitive.

■ Integrate with the PI System.

■ Easy to deploy.

■ Easy to use.

After considering various wireless systems, NRG selected Petasense's vibration motes and Asset Reliability & Optimization (ARO) system to monitor 13 critical pumps and motors, including steam- and motor-driven boiler feed pumps and condensate pumps, and a startup boiler feed pump that runs only during startup and shutdown. Continuous monitoring of these pumps provided consistency in the vibration readings, as several of the pumps operate at variable speeds and loads. The periodic nature of walkaround systems makes it difficult to take readings under consistent operating conditions. By including process data such as flow, speed, and load from PI in the analysis, the PdM team is able to more accurately determine an underlying failure.

The vibration motes collect vibration and temperature eight times per day, which is sent to Petasense's cloud-based software to provide on-demand alerts to the PdM team of developing problems. This allows for improved efficiency, as an alarm prompts examination. The ARO cloud provides a simple, trendable Asset Health Score, as well as detailed diagnostic tools needed to diagnose the underlying cause of problems.

The biggest benefit to the PdM team was peace of mind that comes with continuous data collection. During the first five months collecting and analyzing data, the PdM team achieved its goal of zero missed starts during the peak summer months. After five months, the units were taken offline to balance generating portfolio with demand.

Developing Greater Insights from Data Collected

The team saw the frequent data collection as a way to improve plant performance. Each triaxial sensor provided multiple data points that could be brought in as tags to the PI Asset Framework. They uploaded all vibration and temperature data points collected from 29 sensors over a five-month summer run season.

With the combined historical asset condition and process data, the impact of process variation is visible on a single screen. For example, there is simultaneously visibility of the flowrate of water through a specific pump, and how it is impacting the vibration on that asset and overall power generated (Figure 1). This provides a visual and easy way to troubleshoot when vibration starts to increase–if there have been no process changes, they can determine that the asset may be developing a problem.

1. This example shows data within the OSIsoft PI System that includes the combination of vibration and process data on a single screen. Courtesy: NRG Energy

Another area that continuous asset condition data can improve NRG's PdM program is in determining whether a pump is running on its Best Efficiency Point (BEP). The BEP is defined as the flow at which the pump operates at the optimum efficiency for a given impeller diameter. When a pump is operating off its design curve, it will create problems such as premature bearing and seal wear, and cavitation.

As a pump is cycled up and down, it is often running off the design curve, meaning it is not moving enough, or moving too much, water. NRG can see by looking at the vibration during ramping between low and high loads whether the pump is off its design curve and modify operating parameters or alert operators to manage pump operation appropriately until the problem can be corrected.

Next Steps

Every summer run season comes with new challenges. NRG's PdM team will continue using wireless technology to provide more frequent readings and complement other systems. The more frequent nature of continuous wireless vibration data helps to provide insights that would be difficult to obtain with a walkaround program and provides confidence that there won't be any missed starts.

As NRG looks to IIoT technology to improve generating performance, the company is considering pushing live asset condition data to PI instead of batch data. This would allow closer monitoring and increase the value of collecting data–using data to move beyond failure prediction to operational improvement.

–Calvin Arnold is predictive maintenance supervisor at NRG, Mitch Levings is an engineer at NRG, and Juli Iacuaniello is marketing director with Petasense.

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