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Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains xii, 102 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 80-81).

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains xii, 121 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 117-119).

Thesis (M.E.)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (p. 194-196). Also available via the World Wide Web.

Thesis (Ph.D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Dimitri Mavris; Committee Member: Erwing Calleros; Committee Member: Hongmei Chen; Committee Member: Mark Waters; Committee Member: Vitali Volovoi.

This thesis focuses on the application of model predictive control approaches to optimize the combined cycle power plants start-ups. Generally, the optimization of start-up is a very problematic issue that poses significant challenges. The development of the proposed approaches is progressive. In the first part a physical model of plant is developed and adapted to optimization purposes, by using a methodology which transforms Modelica model components into optimization-oriented models. By applying this methodology, a library suitable for optimization purposes has been built.In the second part, based on the developed model, an optimization procedure to improve the performances of the start-up phases is suggested. The proposed solution optimizes, in continuous time, the load profile of the turbines, by seeking in specific sets of functions. The optimal profile is derived by considering that this profile can be described by a parameterized function whose parameters are computed by solving a constrained optimal control problem. In the last part, the open-loop optimization procedure has been integrated into a receding horizon control strategy. This strategy represents a robust solution against perturbation and models errors, and enables to improve the trade-off between computation time and optimality of the solution. Nevertheless, the control approach leads to a significant computation time. In order to obtain real-time implementable results, a hierarchical model predictive control structure with two layers, working at different time scales and over different prediction horizons, has been proposed.

Combined cycle power plants haven becoming one of the mainstream power plants in the twenty-one century. The emergence of high 600¢J exhaust temperature of the gas turbine, due to the recent rapid enhancement of aerospace material and blade cooling methods, upgrades the gas turbine from low efficiency dual pressure non-reheat unit to high efficiency triple pressure reheat combined cycle power plants. In addition, the increase of turbine inlet temperature by 10~15¢J every year leads to the renewal of the advanced models gas turbine less than ten years. There are three-turbine inlet temperature (TIT) definitions in the gas turbine: (1) TA defines firing temperature as the mass flow mean total temperature before the first-stage stationary diagram edge plane.( Westinghouse or MHI product) (2) TB defines fire temperature as the mass flow mean total temperature at the first-stage nozzle trailing edge plane, ( GE product). (3) TC defines ISO firing temperature; it is a stoichiometric combustion temperature. It is not a physical temperature. ( Siemens ¡® Alstom ABB product). This study shows how to calculate compressor inlet mass flow balance, turbine power balance and heat balance on the combustion chamber system. In order to prove correctness of the balance equation, the data are taken from the heat balance diagram and acceptance test of Nan-pu power station combined cycle. The result shows that the study is sultable for application of the optimum analysis for CCPP operation performance. This type of combined cycle power plant suits not only for the base-load but also for the cycling-load operation.

M.Sc. / The increased demand for electricity in South Africa is expected to exceed supply between 2004 and 2007. Electricity supply options in the country would be further complicated by the fact that older power stations would reach the end of their design life beyond the year 2025. In light of this and considering the long lead times required for the commissioning of new plants, new power supply options need to be proactively investigated. The environmental impacts associated with coal-fired generation of electricity have resulted in increased global concern over the past decade. To reduce these impacts, new technologies have been identified to help provide electricity from fossil fuels. The alternatives considered are gas-fired generation technologies and the Integrated Gasification Combined Cycle (IGCC). This study attempts to document and understand the environmental aspects related to gas-fired and IGCC electricity generation and evaluate their advantages in comparison to conventional pulverised coal fired power generation. The options that could be utilised to make fossil fuel electricity generation more environmentally friendly, whilst remaining economically feasible, were also evaluated. Gas-fired electricity generation is extremely successful as electricity generation systems in the world due to inherently low levels of emissions, high efficiencies, fuel flexibility and reduced demand on finite resources. Associated benefits of a Combined Cycle Gas Turbine (CCGT) are lower operating costs due to the reduced water consumption, smaller equipment size and a reduction in the wastewater that has to be treated before being returned to the environment. A CCGT plant requires less cooling water and can be located on a smaller area than a conventional Pulverised Fuel (PF) power station of the same capacity. All these factors reduce the burden on the environment. A CCGT also employs processes that utilises the energy of the fuel more efficiently, with the current efficiencies approaching 60%. Instead of simply being discharged into the atmosphere, the gas turbines’ exhaust gas heat is used to produce additional output in combination with a Heat Recovery Steam Generator (HRSG) and a steam turbine. Furthermore, as finite resources become increasingly scarce and energy has to be used as wisely as possible, generating electricity economically and in an ecologically sound manner is of the utmost importance. The clean, reliable operation of gas-fired generation systems with significantly reduced noise levels and their compact design makes their operation feasible in heavily populated areas, where electricity is needed most. At the same time, energy can be consumed in whatever form needed, i.e. as electricity, heat or steam. The dependence of the South African economy on cheap coal ensures that it will remain a vital component of future electricity generation options in the country. This dominance of coal-fired generation in the country is responsible for South Africa’s title as the largest generator of carbon dioxide (CO2) emissions on the continent and the country could possibly be requested to reduce its CO2 emissions at the next international meeting of signatories to the Kyoto Protocol. Carbon dioxide emissions can be reduced by utilising gas-fired generation technologies. However, the uncertainty and costs associated with natural gas in South Africa hampers the implementation of this technology. There are currently a number of initiatives surrounding the development of natural gas in the country, viz. the Pande and Temane projects in Mozambique and the Kudu project in Namibia, and this is likely to positively influence the choice of fuel utilised for electricity generation in the future. The economic viability of these projects would be further enhanced through the obtaining of Clean Development Mechanism (CDM) credits for greenhouse gases (GHG) emissions reduction. Alternatively, more efficient methods of generating electricity from coal must be developed and implemented. IGCC is capable of achieving this because of the high efficiencies associated with the combined cycle component of the technology. These higher efficiencies result in reduced emissions to the atmosphere for an equivalent unit of electricity generated from a PF station. An IGCC system can be successful in South Africa in that it combines the benefits of utilising gas-fired electricity generation systems whilst utilising economically feasible fuel, i.e. coal. IGCC systems can economically meet strict air pollution emission standards, produce water effluent within environmental limits, produce an environmentally benign slag, with good potential as a saleable by-product, and recover a valuable sulphur commodity by-product. Life-cycle analyses performed on IGCC power plants have identified CO2 release and natural resource depletion as their most significant positive lifecycle impacts, which testifies to the IGCC’s low pollutant releases and benign by-products. Recent studies have also shown that these plants can be built to efficiently accommodate future CO2 capture technology that could further reduce environmental impacts. The outstanding environmental performance of IGCC makes it an excellent technology for the clean production of electricity. IGCC systems also provide flexibility in the production of a wide range of products including electricity, fuels, chemicals, hydrogen, and steam, while utilizing low-cost, widely available feedstocks. Coal-based gasification systems provide an energy production alternative that is more efficient and environmentally friendly than competing coalfuelled technologies. The obstacle to the large-scale implementation of this technology in the country is the high costs associated with the technology. CDM credits and by-products sales could possible enhance the viability of implementing these technologies in South Africa.

Cette thèse porte sur l’application des approches de commande prédictive pour l’optimisation des démarrages des centrales à cycles combinés. Il s’agit d’une problématique à fort enjeu qui pose des défis importants. L’élaboration des approches est progressive. Dans une première partie un modèle de centrale est construit et adapté à l’optimisation, en utilisant une méthodologie qui transforme des modèles physiques Modelica conçus pour la simulation en des modèles pour l’optimisation. Cette méthodologie a permis de construire une bibliothèque adaptée à l’optimisation. La suite des travaux porte sur l’utilisation du modèle afin d’optimiser phase par phase les performances du démarrage. La solution proposée optimise, en temps continu, le profil de charge des turbines en recherchant dans des ensembles de fonctions particulières. Le profil optimal est déterminé en considérant que celui-ci peut être décrit par une fonction paramétrée dont les paramètres sont calculés en résolvant un problème de commande optimale sous contraintes. La dernière partie des travaux consiste à intégrer cette démarche d’optimisation à temps continu dans une stratégie de commande à horizon glissant. Cette approche permet d’une part de corriger les dérives liées aux erreurs de modèles et aux perturbations, et d’autre part, d’améliorer le compromis entre le temps de calcul et l’optimalité de la solution. Cette approche de commande conduit cependant à des temps de calcul importants. Afin de réduire le temps de calcul, une structure de commande prédictive hiérarchisée avec deux niveaux, en travaillant à des échelles de temps et sur des horizons différents, a été proposée. / This thesis focuses on the application of model predictive control approaches to optimize the combined cycle power plants start-ups. Generally, the optimization of start-up is a very problematic issue that poses significant challenges. The development of the proposed approaches is progressive. In the first part a physical model of plant is developed and adapted to optimization purposes, by using a methodology which transforms Modelica model components into optimization-oriented models. By applying this methodology, a library suitable for optimization purposes has been built.In the second part, based on the developed model, an optimization procedure to improve the performances of the start-up phases is suggested. The proposed solution optimizes, in continuous time, the load profile of the turbines, by seeking in specific sets of functions. The optimal profile is derived by considering that this profile can be described by a parameterized function whose parameters are computed by solving a constrained optimal control problem. In the last part, the open-loop optimization procedure has been integrated into a receding horizon control strategy. This strategy represents a robust solution against perturbation and models errors, and enables to improve the trade-off between computation time and optimality of the solution. Nevertheless, the control approach leads to a significant computation time. In order to obtain real-time implementable results, a hierarchical model predictive control structure with two layers, working at different time scales and over different prediction horizons, has been proposed.

Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principle component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation.