摘要: In the Mianhuatan hydro Power Plant,China,there is a growing interest in the investigation concerning the static and dynamic behaviour of the power plants.For this investigation,correctly developed nonlinear dynamic models of the ... 展开 In the Mianhuatan hydro Power Plant,China,there is a growing interest in the investigation concerning the static and dynamic behaviour of the power plants.For this investigation,correctly developed nonlinear dynamic models of the power plants are necessary.These models have to be reliable for all operating conditions of the system from zero to full loading.On the other hand,the complexity and the order of the models should not be too high,because,in some investigations,for example restoration scenarios,a lot of power plants will act together in reality oriented simulations. The identification of the physical parameters of a synchronous generator was presented.The method is based on the fact that for the synchronous generator well-defined linear and nonlinear model structures exist.The Heffron-Phillips model is used to extract physical parameters from the identified transfer functions.The proposed method requires very simple test on the machine during normal operating conditions. In this study,two identification methods(Particle swarm optimization,PSO and Genetic Algorithms,GA)have been used for the parameter identification of the hydro power plant,including the synchronous generator and its control(Excitation system and Govemor-Turbine System).In these methods,no matter what kind of input signals are used,the PSO can always obtain satisfactory identification results even if a strong noise exists. It is clear from the results presented in this Dissertation that the advantages of PSO over other traditional optimization techniques can he summarized as follows: a)PSO is a population-based search algorithm(i.e.,PSO works in an implicit parallel form).This property ensures that PSO is less susceptible by the local minimum trapping. b)PSO uses payoff performance index(or objective function)information to guide the search in the problem space.Therefore,PSO can easily deal with non-differentiable objective functions.Additionally,this property relieves PSO from assumptions and approximations,which are often required for the traditional optimization models. c)PSO works on the probabilistic transition rules and not deterministic rules.Hence,it is a kind of stochastic optimization algorithm that can search a complicated and uncertain area.This makes PSO more flexible than conventional methods. d)Unlike Genetic Algorithm(GA)and other heuristic algorithms,PSO has the flexibility to control the balance between the global and local exploration in the search space.This unique feature of the PSO overcomes the premature convergence problem and enhances the search capability. e)Unlike the traditional methods,the solution quality of the proposed approach doesn't rely on the initial population.Starting anywhere in the search space,the algorithm can ensure the convergence of the optimal solution. In this dissertation,the modeling of hydro power plants is presented.It has been proved that the practical and user-friendly mathematical model of the complex system as it is a hydro power plant can be created. The results of the work are very useful for the Mianhuatan hydro Power Plant.The results will be used for investigations in the following fields: Black Start Capabilities after Black Out Behavior in isolated operation Primary and secondary power reserve capabilities Reduction of losses in the plants Overall dynamic behavior of the whole system including stability investigations. Furthermore,these models can be practically used for expert's analysis and staff training in Mianhuatan hydro Power Plant. 收起