**1. Introduction**

212 Efficiency, Performance and Robustness of Gas Turbines

Loboda, I.; Yepifanov, S. & Feldshteyn, Ya. (2007). A generalized fault classification for gas

Loboda, I.; Yepifanov, S. & Feldshteyn, Ya. (2009). Diagnostic analysis of maintenance data

MacIsaac, B. D. & Muir, D. F. (1991). Lessons learned in gas turbine performance analysis,

Meher-Homji, C.B.; Chaker, M.A. & Motivwala, H.M. (2001). Gas turbine performance

Mesbahi, E.; Assadi, M. et al. (2001). A unique correction tchnique for vaporative gas turbine

Ogaji, S.O.T.; Li, Y. G.; Sampath, S. et al. (2003). Gas path fault diagnosis of a turbofan

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Rao, B.K.N. (1996). *Handbook of Condition Monitoring*, Elsevier Advanced Technology, Oxford Roemer, M.J. & Kacprzynski, G.J. (2000) Advanced diagnostics and prognostics for gas turbine

Romessis, C. & Mathioudakis, K. (2006). Bayesian network approach for gas path fault diagnosis, *Journal of Engineering for Gas Turbines and Power*, Vol. 128, No. 1, pp. 64-72. Sampath, S. & Singh, R. (2006). An integrated fault diagnostics model using genetic

Saravanamuttoo, H. I. H. & MacIsaac, B. D. (1983). Thermodynamic models for pipeline gas

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*Conference*, pp. 73-89, British Hydraulic Research Association, UK

USA, June 4-7, 2001, ASME Paper 2001-GT-0008

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methodologies in gas turbine performance diagnostics: a comparative study,

In México, near 15% of the installed electrical energy of CFE, the Mexican Utility Company is based on gas turbine plants. The economical and performance results of a power plant are related to different strategies like modernisation, management, and the training of their operators.

The Advanced Training Systems and Simulation Department (GSACyS) of the Electrical Research Institute (IIE) in México is a group specialised in training simulators that designs and implements tools and methodologies to support the simulators development, exploitation, and maintenance. The GSACyS has developed diverse works related with the training. The main covered areas by the IIE developments are: computer based training systems, test equipment simulators, and simulators for operators training.

To use real time full scope simulators is one of the most effective and secure way to train power plant operators. According to Hoffman (1995), by using simulators the operators can learn how to operate the power plant more efficiently. In accordance with Fray and Divakaruni (1995), even not full scope simulators are successfully used for operators training.

Some advantages of using simulators for training are the ability to train on malfunctions, transients and accidents; the reduction of risks of plant equipment and personnel; the ability to train personnel on actual (reproduced) plant events; a broader range of personnel can receive effective training, and eventually, high standard individualised instruction or selftraining (with simulation devices planned with these capabilities).

In the case of simulators, situations "what would have happened if…" arises when a cost benefit analysis is sought. A classical analysis for fuel power plants simulators (Epri, 1993) identified profit of simulators in four classes: availability savings, thermal performance savings, component life savings, and environmental compliance savings. A payback of about three months was estimated. Most often, the justification for acquiring an operators training simulator is based on estimating the reduction in losses (Hosseinpour and Hajihosseini, 2009). It is not difficult to probe the benefits for high-capacity plants where savings means millions of dollars for a few days of un-productivity. Besides, the ability of the simulator to verify the automation system and provide operators with a better

Models for Training on a Gas Turbine Power Plant 215

Ideal gas (IG) and perfect combustion (PX). Based on Simulink. They claim that the model may be utilised to represent plants with different

One-dim empirical model coupled with a

a more detailed numerical model.

Detailed model based on physical principles. No details are given concerning the independent

commercial network solver (flows and pressures). The simplified model predicts the behaviour near

IG with no gas components. Input data were the ambient conditions and the air cooling system configuration. Model validated against plant data.

Output turbine temperatures are empirical enthalpy functions. Inputs are variables like efficiencies, some pressure drops, temperatures, etc. This approach is not useful for a training

Model adjusts the gradient of the generated power change as a function of the weather forecast. Authors indicate that the results have to be compared with the real plant and that that main problem is to develop highly accurate

Four different gas models were used: three based on gas ideal behaviour with different specific heat *Cp* evaluation and one using real gas model with

Combustion was not simulated. Authors say that a good model is needed to reproduce the correct

IG with detailed modelling of the flow through the equipment, heat transfer phenomena and basing the process on a temperature-entropy

Simulink used as platform. The governor system model and a simple machine infinite bus were considered. Model was validated against real data. No details of the combustor model are

thermodynamic properties from tables.

thermodynamic behaviour of the fluid.

characteristics and sizes.

variables.

simulator.

plant model.

diagram.

mentioned.

**(Reference) Framework (Application) General Approach and Commentary** 

faults like compressor fouling PX without heat losses.

Dynamic mathematical model of a generic cogeneration plant to evaluate the influence of small gas turbines in an interconnected

component (helium) to design a

Software diagnosis to detecting

To solve "practical problems" being experienced on a particular

gas turbine combustor.

To study the influence of different air cooling systems

Desktop model for excel to represent a standard air Brayton cycle considering five gas components and combustion stoichiometrics with possibilities

Model based on IG expansion and compression to optimise the fuel consumption of a combined cycle power plant when the power has to be changed.

Four stage gas turbine model to predict the overall turbine performance based on three-dim Navier-Stokes equations. Coolant injections, cavity purge flows and leakage flows were included.

Model designed for optimisation running around the full load point ignoring combustion.

To analyse the dynamical behaviour of industrial electrical

Table 1. Summary of previous published works.

power system.

of oxygen excess.

electric network.

control system.

Gas turbine of only one

**Authors** 

Banetta *et al*., 2001

Kikstra and Verkooijen, 2002

Ghadimi *et al*., 2005

Gouws *et al*., 2006

Jaber *et al*., 2007

Zhu and Frey, 2007

Kaproń and Wydra, 2008

Rubechini e*t al.*, 2008

Chen *et al.*, 2009

Watanabe *et al*., 2010

understanding of a new process must be addressed. Using a simulator help operators to improve the skills to bring the plant up and down, thus shortening start-ups significantly and improving the proficiency of less-experienced operators in existing plants.

In 2000 the CFE initiated the exploitation of a Combined Cycle Power Plant Simulator (CCS) developed by the IIE based on ProTRAX, a commercial tool to construct simulators. There is no full access to the source ProTRAX programs and 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: first the Gas Turbine (GT) part followed by the Heat Recovery Steam Generation (HRSG) part.

In this chapter a summary of the GT simulator development and its modelling characteristics are described. Stochastic and discrete events models are not considered, but deterministic models of industrial processes are contemplated.
