Completing a power plant’s start-up and commissioning usually means pushing the prime contractor to wrap up the remaining punch list items and getting the new operators trained. Staffers are tired of the long hours they’ve put in and are looking forward to settling into a work routine.Just when the job site is beginning to look like an operating plant, a group of engineers arrives with laptops in hand, commandeers the only spare desk in the control room, and begins to unpack boxes of precision instruments.
In a fit of controlled confusion, the engineers install the instruments, find primary flow elements, and make the required connections. Wires are dragged back to the control room and terminated at a row of neatly arranged laptops.
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When the test begins, the test engineers stare at their monitors as if they were watching the Super Bowl and trade comments in some sort of techno-geek language. The plant performance test has begun (Figure 1). Trading spaces. This is a typical setup of data acquisition computers used during a plant performance test. Courtesy: McHale & Associates Anatomy of a testThe type and extent of plant performance testing activities are typically driven by the project specifications or the turnkey contract. They also usually are linked to a key progress payment milestone, although the value of the tests goes well beyond legalese.
The typical test is designed to verify power and heat rate guarantees that are pegged to an agreed-upon set of operating conditions. Sounds simple, right? But the behind-the-scenes work to prepare for a test on which perhaps millions of dollars are at stake beyond the contract guarantees almost certainly exceeds your expectations (see box). Performance test economics are overpoweringConsider a 500-MW facility with a heat rate of 7,000 Btu/kWh. When operating at baseload with an 80% capacity factor, the plant will consume over 24 million mmBtu per year. At a fuel cost of $8/mmBtu, that’s nearly $200 million in fuel costs for the year.If an instrumentation or control error raises the heat rate of the facility by 0.5%, that would cost the plant an additional $1 million each year.
Understanding test uncertaintyUncertainty is a measure of the quality of the test or calculation result. A pretest uncertainty analysis can be used to design a test to meet predefined uncertainty limits. A post-test uncertainty analysis should be performed to verify that those uncertainty limits were met and to determine the impact of any random scatter recorded in the test data.Each input to the calculation must be analyzed for its impact on the final result. This impact is identified as the sensitivity of the result to that input.
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For example, if inlet air temperature changes by 3 degrees F, and the corrected output changes by 1%, the sensitivity is 1% per 3 degrees F or 0.33%/degree F.The instrumentation information is used to identify the systematic error potential for each input. For example, a precision 4-wire resistance-temperature detector can measure inlet air temperature with an accuracy of +/- 0.18F, based on information provided by the manufacturer and as confirmed during periodic calibrations.During a test run, multiple recordings are made for any given parameter, and there will be scatter in the data. The amount of scatter in the data is an indication of the random error potential for each input. For example, during a 1-hour test run, the inlet air temperature may be recorded as an average of 75F, with a standard deviation in the measurements of 0.6F.If more than one sensor is used to measure a parameter, there also will be variances between sensors based on location. These variances may be due to the variances either in the instrumentation or in the actual parameter measured. For example, if air temperature is being measured by an array of sensors, there may be effects due to ground warming or exhaust vents in the area, either of which would affect the uncertainty of the bulk average measurement. These variances will affect the average and standard deviation values for that parameter.
The analysis of the reliability and availability of power plants is frequently based on simple indexes that do not take into account the criticality of some failures used for availability analysis. This criticality should be evaluated based on concepts of reliability which consider the effect of a component failure on the performance of the entire plant.