The $11.4
billion worth of non-aviation gas turbines produced in 2008, $9.6 billion—more than
80 percent—were for electrical generation (Langston, 2008). Particularly, in
Mexico, about 15% of the installed electrical energy (no counting the
electricity generated for internal consuming by big enterprises) is based on
gas turbine plants (CFE web page), either working alone or in combined cycle
power plants (and 8% produced directly by gas turbines) that offers an
important roll in improving power plant efficiency with its corresponding gains
in environmental performance (Rice, 2004).
The economical and performance results
of a power plant, including those based on gas turbines, are directly related
to different strategies like modernisation, management, and, in particular, the
training of their operators. Although the proportion that corresponds to the training
is difficult to be assessed, there exists a feedback from the plant’s directors
about improvement in speed of response, analysis of diverse situations, control
of operational parameters, among other operator’s skills, due to the training
of the operation personnel with a full scope simulator. In general, all these
improvements lead to a greater reliable installation. The Comisión Federal de
Electricidad (CFE1, the Mexican Utility Company) generates, transmits, distributes and commercialises
electric energy for about 27.1 millions of clients that represent almost 80
millions of people. About one million of new costumers are annually added.
Basically, the infrastructure to generate the electric energy is composed by 177
centrals with an installed capacity of 50,248 MW (the CFE produces 38,791 MW
and the independent producers 11,457 MW).
The use of real time full
scope simulators had proven trough the years, to be one of the most effective
and confident ways for training power plant operators. According to Hoffman (1995),
using simulators the operators can learn how to operate the power plant more efficiently
during a lowering of the heat rate and the reducing of the power required by
the auxiliary equipment. According to Fray and Divakaruni (1995), even not full
scope simulators are used successfully for operators’ training.
1. Some acronyms are written
after their name or phrase spelling in Spanish. A full definition of the used acronyms
in this chapter is listed in Section 13.
The Simulation Department (SD) belongs to the Electrical Research
Institute (IIE) and is a group specialised in training simulators that design
and implement tools and methodologies to support the simulators development,
exploiting and maintenance. In 2000 the CFE initiated the operation of the
Simulator of a Combined Cycle unit (SCC) developed by the IIE based on ProTRAX,
a commercial tool to construct simulators. However, because there is no full
access to the source programs, the CFE determined to have a new combined cycle
simulator using the open architecture of the IIE products. The new simulator
was decided to be constructed in two stages: the gas-turbine part and the steam-heat
recovery part. In this chapter the gas-turbine simulator development and characteristics
are described.
2.
Modelling approaches and previous works
There is not
a universal method to simulate a process. The approach depends on the use the model
will be intended for and the way it is formulated. A model may be used for
different purposes like design, analysis, optimisation, education, training,
etc. The modelling techniques may vary from very detailed physical models
(governing principles) like differences or finite elements, to empirical models
like curves fitting, in the extremes, with the real time modelling approach
(for operators’ training) somewhere in the middle. In fact there would be a
huge task trying to classify the different ways a model may be designed. Here,
deterministic models of industrial processes are considered (ignoring the
stochastic and discrete events models). The goal is to reproduce the behaviour
of, at least, the variables reported in the control station of a gas turbine
power plant operator in such a way the operator cannot distinguish between the
real plant and the simulator. Thus, this reproduction may be made considering
both, the value of the variables and their dynamics. The approach was a
sequential solution with a lumping parameters approach (non-linear dynamic
mathematical system based on discrete time). A description of the technique to formulate
and solve the models is explained below in this chapter. To accomplish with the
described goal, the “ANSI/ISA S77.20-1993 Fossil-Fuel Power Plant Simulators
Functional Requirements” norm was adopted as a design specification. The models
for operation training are not frequently reported in the literature because
they belong to companies that provide the training or development simulators
services and it is proprietary information (see, for example, Vieira et al.,
2008). Besides, Colonna & van Putten (2007) list various limitations on
this software. Nevertheless, a comparison between the approaches of the IIE and
other simulators developer was made, showing the first to having better results
(Roldán-Villasana & Mendoza-Alegría, 2006). Some gas turbine models have
been reported to be used in different applications. A common approach is to
consider the work fluid as an ideal gas. All the revised works report to have a
gas turbine system like the presented in Figure
1. A dynamic mathematical model of a generic
cogeneration plant was made by Banetta et al. (2001) to evaluate the influence
of small gas turbines in an interconnected electric network. They used Simulink
as platform and they claim that the model may be utilised to represent plants
with very different characteristics and sizes, although the ideal gas
assumption was used, the combustor behaves ideally and no thermodynamic
properties are employed.
Kikstra
& Verkooijen (2002) present a model based on physical principles (very
detailed) for a gas turbine of only one component (helium). The model was
developed to design a control system. No details are given concerning the
independent variables. The model validation was performed comparing the results
with another code (Relap).
Ghadimi et al. (2005) designed a
model based on ideal gas to diagnostic software capable of detecting faults
like compressor fouling. The combustion was considered perfect and no heat
losses were modelled. The fouling of the compressor was widely studied. No information
was provided regarding the input variables. Jaber et al. (2007) developed a
model to study the influence of different air cooling systems. They validated
the model against plant data. An ideal gas model was considered and the gas
composition was not included. The input data were the ambient conditions and
the air cooling system configuration.
The
combustion was simulated with a temperature increase of the gas as a function
of the mass flow and the fuel high heating value. A model for desktop for excel
was elaborated by Zhu & Frey (2007) to represent a standard air Brayton
cycle. The combustor model considers five components and the combustion reaction
stoichiometrics with possibilities of excess of oxygen. Instead using well
known thermodynamic properties, the output temperatures of the turbine are a
second degree equation in function of the enthalpy. The inputs are variables
like efficiencies, some pressure drops, temperatures, etc. This approach is not
useful for a training simulator. A model to diagnose the operation of combined
cycle power plants was designed by González-Santaló et al. (2007). The goal was
to compare the real plant data with those produced by a model that reproduces
the plant variables at ideal conditions. The combustor was modelled considering
a complete combustion like a difference between the enthalpy of formation of
the reactants and the combustion products. Compressors and turbines take into account
the efficiencies (adjusted with plant results) and the enthalpies of the gases
(but no information was provided how the enthalpies are calculated as a
function of measured plant data). Kaproń & Wydra (2008) designed a model
based on gas ideal expansion and compression to optimise the fuel consumption
of a combined cycle power plant when the power has to be changed by adjusting
the gradient of the generated power change as a function of the weather
forecast.
In the conclusions the authors point that the
results have to be confirmed on the real plant and that main problem is to
develop highly accurate plant model. Rubechini et al. (2008) simulated a four
stage gas turbine using a fully three-dim, multistage, Navier-Stokes analyses
to predict the overall turbine performance. Coolant injections, cavity purge
flows and leakage flows were included. Four different gas models were used:
three based on gas ideal behaviour (the specific heat Cp evaluation was the
difference among them) and one using real gas model with thermodynamic
properties (TP) from tables as basis of the modelling. The combustion was not
simulated. The conclusion was that a good model has to reproduce the correct
thermodynamic behaviour of the fluid.
Even when detailed modelling
of the flow through the equipment, heat transfer phenomena and basing the
process on a temperature-entropy diagram, the ideal gas assumption was present
(Chen et al., 2009). In this case the gas composition was neglected,
(considering only an increase of the temperature) and the model, designed for
optimisation, runs around the full load point. Watanabe et al. (2010) used
Simulink to support a model to analyse the dynamical behaviour of industrial
electrical power system. An ideal gas approach was used. The governor system model
and a simple machine infinite bus were considered (with an automatic voltage regulator
model). The model was validated against real data. No details of the combustor model
are mentioned. None of the works revised here, mentioned anything about real
time execution. In the present work, the total plant was simulated, including
the combustion products and all the auxiliary systems to consider all the
variables that the operator may see in his 20 control screens and all the
combinations he desires to configure tendency graphs. For example, the set
compressor- combustor –turbine was simulated considering the schematic
presented. The real time execution that is required for a training simulator is
accomplished by the IIE simulator.
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