Enhancing power plant reliability: The role of predictive maintenance and data modeling

In the era of cloud computing, the immense potential of planned predictive maintenance (PPM) cannot be denied. Yet, the use of machine learning and analytics does not necessarily guarantee actionable insights. Despite the clearly huge opportunities for added value presented by digitization, effective implementation of PPM in today’s combined cycle power plants (CCPPs) will require more than just access to technology.
 
Cracking is one such challenge. It’s found across many critical power plant components, including hot reheat (HRH) bypass valves and attemperators, but is often overlooked by plant managers in favor of larger, more expensive downstream assets. Irregular cycling, however, is now making cracking issues worse – especially as plant restarts and load shifting become common features of generating power today.
 
Indeed, the effectiveness of PPM for cracking and similar issues is determined by various factors. For example, democratizing the maintenance process through cloud-based solutions runs the risk of creating a disconnect between digital insights and real-world scenarios. What is on screen may not translate to what is happening in the plant, meaning engineering consultancy will still be needed to accurately diagnose plant issues.
 
Similarly, predictive maintenance predominantly relies on actual operating data rather than asset runtime alone. Though the technology may record an abundance of data, more scrutiny may be placed on the tools used for collection and not the underlying trends they should uncover. The challenge, therefore, lies not in gathering more information but in accessing and interpreting this data effectively.

Identifying Cracks

Given these knowledge gaps, a comprehensive approach to asset management that integrates modeling with knowledge-based diagnostics may be required to identify specific maintenance challenges. The comparison of historical CCPP data and against system-wide, physics-based analysis enables maintenance teams to better identify potential issues threatening structural integrity, including cracks. This pivotal part of preventative maintenance can therefore be carried out more effectively, allowing technicians to take preemptive action over potential cracks and reduce the risk of unplanned downtime.
 
This is integral, especially given the significant risks thermal stresses and the ensuing cracks in pressure boundaries can pose, particularly in areas where water injection regulates temperature. Components affected by this phenomenon, including steam turbine bypass systems and interstage attemperators, are often overshadowed by larger assets but play a crucial role in plant operations nonetheless. If neglected, these impaired parts can have a catastrophic impact on critical assets, which is why early detection and remedial work is so important.
 
The increased intermittency of renewables also poses a problem to plant managers, as CCPPs cycle power up and down more frequently and place systems originally designed for consistent 24-hour operation under greater operational stress. Yet renewables are not the only emerging contributor to system fatigue. Valve design, casting quality, and material properties have all been cited by plant teams as factors exacerbating wear and tear on CCPC system components.

Maintenance Fundamentals

Though most CCPP designs are unique with an array of differing components, certain unchanging fundamentals remain that govern best maintenance practice. For instance, by analyzing site performance data in key areas such as hardware, control logic and installation and how they affect each other, the risk of potential failure can be negated regardless of other influencing factors. For example, though a plant's attemperator spray valves may be correctly sized for the application, the system's control logic could be initiating excessive spray when not needed, resulting in massive quench events and, eventually, operational failure.
 
Automation, where feasible, is also recommended to anticipate changing conditions accurately. Many CCPPs still have some degree of manual intervention in the control logic to quickly start up older plants designed for baseload operation, but this can lead to potential issues. Namely, this way of working may result in diminished control and impair maintenance teams’ ability to dynamically react to changing circumstances. By automating in applicable areas, these possible problems can be resolved.

Digital tools plus advanced data modeling

In conclusion, digital tools alone may not be sufficient for effective preventive maintenance or for tackling the complex engineering challenges faced by modern power plants. As reliance on intermittent renewable energy sources grows, combined cycle power plants (CCPPs) are expected to experience more irregular cycling, placing additional stress on critical components - even in the most robust facilities. 
 
To address this, a more integrated strategy that combines advanced data modeling with a knowledge-based diagnostic framework offers a more scalable and proactive solution. By leveraging historical data, expert insights, and predictive analytics, plant managers can more accurately identify the root causes of issues such as component cracking. This enables earlier intervention, better-informed decisions, and more effective risk management - ranging from targeted component replacement to control logic optimization and valve upgrades - ultimately minimizing costly downtime and enhancing overall plant resilience.