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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Nicolas, W. Chbat Rajamani, Ravi Todd, A. Ashley |
| Copyright Year | 1996 |
| Abstract | We show that a neural network can be successfully used in place of an actual model to estimate key unmeasured parameters in a gas turbine. As an example we study the combustion reference temperature, a parameter that is currently estimated via a nonlinear model inside the controller and is used in a number of critical mode-setting functions within the controller such as calculating the fuel-split between various manifolds. We show that a feedforward neural network using simple back propagation learning can be built for estimating combustion reference temperature. The neural network matches the accuracy of the current estimate; and it is more robust to errors in its internal parameters. This is advantageous from the point of view of implementation since a number of errors creep in when running the algorithm on a digital controller, and an estimator that is not robust with respect to such errors can degrade the performance of the whole system. |
| Sponsorship | International Gas Turbine Institute |
| File Format | |
| ISBN | 9780791878767 |
| DOI | 10.1115/96-GT-316 |
| Volume Number | Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; General |
| Conference Proceedings | ASME 1996 International Gas Turbine and Aeroengine Congress and Exhibition |
| Language | English |
| Publisher Date | 1996-06-10 |
| Publisher Place | Birmingham, UK |
| Access Restriction | Open |
| Subject Keyword | Manifolds Cycles Errors Temperature Gas turbines Algorithms Creep Artificial neural networks Control equipment Feedforward control Combustion Fuels |
| Content Type | Text |
| Resource Type | Article |
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