Remote Thermal Performance Monitoring: Or, How to Mitigate Degradation Early On

by Sanjeev Jolly, P.E. – Senior Engineer, Engineering Services, NAES Corporation

Thermal performance monitoring is a powerful tool for monitoring plant performance on a continuous basis by focusing on two key performance indicators (KPIs) – output and heat rate. Any issue with a gas turbine, steam turbine, HRSG, condenser or cooling tower will eventually affect the plant electrical output or heat rate or both. It’s therefore critical to analyze the performance of individual components and their contribution to the overall performance. Equipment usually performs ‘as expected’ when it is new and clean, but overall performance degrades with time.

Degradation is classified as ‘recoverable’ or ‘non-recoverable.’ Recoverable degradation can be restored by taking corrective action at the plant level without major modifications – for example, performing a waterwash to address compressor fouling. If there is compressor or blade erosion, on the other hand, it falls under the non-recoverable category, because performance can only be restored by replacing or blending the blades.

But how can you know whether your units are degrading, to what degree, what is causing it, and is there a way to stop it – or better yet, reverse it? When properly implemented, thermal performance monitoring is like conducting a continuous capacity test, except that all the instruments used are station instruments. You can also use it to determine the authenticity of some of your measurements by conducting heat and mass balancing to detect faulty readings. You can better understand the component performance by calculating parameters that are important but not directly measured: air mass flow, firing temperature and component efficiencies, among others.

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Even minor changes in performance can directly affect the bottom line – and, if overlooked, they develop into larger, more costly issues. For example, Table 1 below was generated for an F class turbine based on a 60 percent capacity factor and 5 cents/kWh. It shows convincingly that remote monitoring costs a fraction of the penalty incurred for a single item’s degradation. In addition, even minor findings revealed by a monitoring system would more than offset the cost of installing and implementing it. Early detection, after all, allows you to take corrective action before minor issues escalate into catastrophic failures.

Table 1 – F Class Turbine Operating Costs


A one-time setup of thermal modeling requires the following:

  1. Setup of the model: figures 1 and 2 show a general arrangement for gas turbine and steam bottoming cycle.
  2. A systematic approach to predicting expected performance and selecting a baseline reference case.
  3. Data acquisition and fine-tuning of the model for site-specific operating conditions.

Figure 1: Gas turbine model

Figure 2: Steam bottoming cycle

The thermal performance is evaluated by comparing the measured performance to expected performance. If the difference between the expected and measured performance is positive, it indicates degradation or shortfall. As an alternative, the measured value can be adjusted to corrected measured value; comparison of this corrected value to the reference value yields the degradation.

There’s another alternative approach that may be easier to understand: Plot the measured value and percentage of deviation, where deviation is the percent of variation from expected performance. A consistent trend in deviation that falls outside an acceptable range would then indicate the degradation.

As the name implies, expected performance refers to the unit performance – without any degradation – at the conditions in which the unit was operated. There are, of course, external conditions that do not originate within the equipment that affect performance. The rating of the gas turbine is established within a given set of the following external conditions:

  • Ambient air temperature, pressure and humidity
  • Inlet and exhaust pressure loss
  • Steam/water injection rates
  • Inlet air cooling
  • Fuel type
  • Heating value
  • Inlet guide vane angle

However, most of the time the unit operates outside these conditions. The expected output needs to be adjusted for each of these external conditions by using correction factors. There are several tools for generating these:

  • OEM-supplied correction curves
  • OEM-supplied software such as GE APPS
  • Professional modeling software such as GateCycle or IPSEPro

If none of these sources is available, use correlations or machine learning that develops correlations by using data from when the unit was operating without degradation – usually from when it was new.

A large amount of data is required to capture seasonal variations and different operational scenarios. The major advantage to using correlations is that it captures the actual unit performance and applies the correlation to all components of the system, some of which may not have correction curves readily available.

The performance monitoring can be done via remote access to data or using data provided in a spreadsheet. The evaluation is then conducted by running the model; any anomalies are pointed out along with a corrective action. After implementation of the corrective action, the data is reevaluated to determine the impact on performance and whether any further action is required.

Most users prefer online monitoring because they can view the results immediately. At NAES, however, we realize that CIP security is an issue. There are a couple of options available that take this into account: a mirror server that duplicates the historian data; or a virtual computer at the site that is used to run the model with restricted access through a VPN connection.

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