The intermittent nature
of renewable energy, variations in energy demand, and fluctuations in oil and
gas prices have all contributed to variable demand for power generation from
coal-burning power plants. The varying demand leads to load-follow and on/off
operations referred to as cycling. Cycling causes transients of properties such
as pressure and temperature within various components of the steam generation
system. The transients can cause increased damage because of fatigue and
creep-fatigue interactions shortening the life of components. The data-driven
model based on artificial neural networks (ANN) is developed for the first time
to estimate properties of the steam generator components during cycling
operations of a power plant. This approach utilizes data from the Coal Creek
Station power plant located in North Dakota, USA collected over 10 years with a
1-hour resolution. Cycling characteristics of the plant are identified using a
time-series of gross power. The ANN model estimates the component properties,
for a given gross power profile and initial conditions, as they vary during
cycling operations. As a representative
example, the ANN estimates are presented for the superheater outlet pressure,
reheater inlet temperature, and flue gas temperature at the air heater inlet.
The changes in these variables as a function of the gross power over the time
duration are compared with measurements to assess the predictive capability of
the model. Mean square errors of 4.49E-04 for superheater outlet pressure,
1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature
at the air heater inlet were observed.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12230978 |
| Date | 06 May 2020 |
| Creators | Abhishek Navarkar (8790188) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/MACHINE_LEARNING_MODEL_FOR_ESTIMATION_OF_SYSTEM_PROPERTIES_DURING_CYCLING_OF_COAL-FIRED_STEAM_GENERATOR/12230978 |
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