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Analysis And Modeling Of Gas And Steam Combined Cycle Systems Based On Gas Turbine

Posted on:2016-10-24Degree:MasterType:Thesis
Country:ChinaCandidate:W LiuFull Text:PDF
GTID:2272330470975798Subject:Control engineering
Abstract/Summary:PDF Full Text Request
In recent years, the environment and resource problem is becoming increasingly serious in our country. Coal is mainly used for power generation as the primary energy in the electric power industry in our country, which costs excessive resource consumption and causes serious environment problems. It is necessary to build gas-steam combined cycle unit, which has the advantages of less pollution, strong peaking capacity and low investment, with the development of the gas turbine and gas resources. The gas turbine affects the whole performance of the gas-steam combined cycle unit as a key component.This thesis analyzed the gas-steam combined cycle unit firstly. The most basic combined cycle unit consists of non-afterburning type HRSG、afterburning type HRSG and the supercharged boiler type combined cycle, and the non-afterburning type HRSG of combined cycle is the most widely used today. M701F3 uniaxial heavy duty gas turbine combined cycle unit in Shenzhen Guangqian power plant, for example, showed the working principle of the main parts in combined cycle unit and its workflows. It indicated that the gas turbine was the biggest difference between combined cycle unit and coal fired units so this thesis will build gas turbine model.It is difficult to build accurate models by using traditional modeling methods, because the gas turbine has strong nonlinearity and complex internal structure. The BP neural network was used to modeling for gas turbine in this paper for its high nonlinear mapping ability and fault tolerance. The model of gas turbine was determined through the method of combining theoretical and experimental based on analyzing the dynamic characteristic of gas turbine, as well as a large amount of field data. This paper chose L-M algorithm rather than traditional BP neural network to train the network for its better learning efficiency and the established model was tested. The model can reflect the dynamic characteristics of the gas turbine correctly and have a high precision through the test. It established a good foundation for the further research and application.
Keywords/Search Tags:Gas-steam combined cycle, Systems analysis, Gas turbine, BP neural network modeling
PDF Full Text Request
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