Parameter Identification Of Hydro Turbine Governing System Based On Immune Algorithm

The 1973 IEEE Committee report "Dynamic Models for Steam and Hydro Turbines in Power System Studies"has been used widely. Since then, modeling requirements have increased greatly and more detailed models have been implemented in the advanced simulation programs. Once a model structure is set, the next step is to derive unknown parameter values in the model structure. This thesis emphasizes on researching a kind of new optimization arithmetic which is called Immune Arithmetic and how to use it in the hydro turbine and governor's parameter identification. Immune Arithmetic (IA) is a random search algorithm which imitates natural immune progress. IA performs on the coding of the parameters and not on the exact parameters; therefore, it dose not depend on the continuity of a parameter, not on the existence of derivatives of the functions, as needed in some conventional optimization algorithms. The population strategy enables IA to search for near-optimal solutions form various directions. Therefore, it can avoid convergence to local minimum or maximum points. In this thesis, the basic principle and arithmetic step are described. Simulation results show that the convergence precision and speed are faster than GA. An immune arithmetic based hydro turbine and governor parameter's identification method are proposed. The detailed procedure is given and the method has been used in an real hydraulic generator located in Fujian Power System. The results of research and application indicate that the method has the advantages of high accuracy and good convenience for operation.