Performance adaptation of gas turbines for power generation applications

One of the greatest challenges that the world is facing is that of providing everyone access to safe and clean energy supplies. Since the liberalization of the electricity market in the UK during the 1990s many combined cycle gas turbine (CCGT) power plants have been developed as these plants are more energy e cient and friendlier to the environment. The core component in a combined cycle plant is the gas turbine. In this project the MEA's Pulrose Power Station CCGT plant is under investigation. This plant consists of two aeroderivative LM2500+ gas turbines of General Electric for producing a total of 84MW power in a combined cycle con guration. Accurate gas turbine performance simulation and adaptation leads to robust diagnostic analysis which saves time and money in a sector where even a percent change in thermal e ciency is translated to thousands of pounds. For satisfying the needs of such a competitive environment various software capabilities have been developed for performance and nancial analysis of a power plant. The software that Cran eld's University has built for performance simulation and diagnostics is PYTHIA. This has a graphical user interface (GUI) and performs thermodynamic calculations through the Turbomatch code, which has an international reputation and experience of 30 years. The limitations that, exclusive manufacturer's property, compressor maps have imposed at o -design performance prediction of a gas turbine have been overcome by the development of a novel o -design performance adaptation method. The new approach is capable of adapting the performance of an engine model to that of a service engine at part load conditions. This method has been developed in visual basic and integrated into PYTHIA's environment. The proposed adaptation method initially generates a series of compressor maps, which in turn provide the performance of the engine model at o -design conditions. Thence from a family of possible solutions the best set of compressor map coe cients is determined through a genetic algorithm optimiser. The genetic algorithm optimisation is based on a maximum tness criterion between the

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