Diagnosis and Supervision of Industrial Gas Turbines
Monitoring of industrial gas turbines is of vital importance, since it gives
valuable information for the customer about maintenance, performance, and
process health. The performance of an industrial gas turbine degrades gradually
due to factors such as environment air pollution, fuel content, and ageing to
mention some of the degradation factors. The compressor in the gas turbine is
especially vulnerable against contaminants in the air since these particles are
stuck at the rotor and stator surface. The loss in compressor performance, due
to fouling, can partially be restored by an on-line/off-line compressor wash. If
the actual health state of the gas turbine is known, it is possible to
efficiently plan the service and maintenance and thereby reduce the
environmental impact and the fuel cost for the customer.
A thermodynamic gas turbine modeling package, called GTLib, is developed in the
equation-based object-oriented modeling language Modelica. Using the GTLib
package, a gas turbine model can be constructed. The gas turbine model can be
used for performance calculation and as a base when diagnosis tests are
generated. These tests can be used in a diagnosis and supervision system to
detect compressor fouling and abrupt sensor faults. One of the benefits with
using GTLib is the ability to model a lean stoichiometric combustion at
different air/fuel ratio. Using the air/fuel ratio concept, an arbitrary number
of gas species in the in-coming air can be considered. The number of equations
is reduced if the air/fuel ratio concept is considered instead of modeling each
gas species separately. The difference in the number of equations is significant
if many gas species are considered.
When the gas turbine components deteriorate, a mismatch between the nominal
performance model and the measurements increase. To handle this, the gas turbine
model is augmented with a number of estimation parameters. These estimation
parameters are used to detect slow deterioration in the gas turbine components
and are estimated with a Constant Gain Extended Kalman Filter (CGEKF). The state
estimator is chosen using structural methods before an index reduction of the
model is performed. Experimental data is investigated and it is shown that the
performance degradation due to compressor fouling can be estimated. After the
compressor is washed, the performance of the compressor is partially restored.
An abrupt sensor fault of 1% of the nominal value is introduced in the
discharge temperature of the compressor. The sensor fault can be detected using
the CUSUM algorithm for change detection.
Finally, the overall thesis contribution is the calculation chain from a
simulation model used for performance calculation to a number of test
quantities used in a diagnosis and supervision system. Since the considered gas
turbine model is a large non-linear DAE model that has unobservable state
variables, the test construction procedure is automatically performed with
developed parsers.
Emil Larsson
2012
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
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