Intelligent Control Theory And Method For Complex Generator System

With the development of power system industrial, the requirement of power energy is more and more, at the same time, the quality requirement of energy supplying and power stablity is higher and higher. Generators are the key equipments for energy supplying, and their performances are very crucial in power system operation. It is a tendency that the generators have higher capacity, this also means that the effective control of generators is more difficult and more important.Firstly, this dissertation introduces the outline of power system control and stability, then presents the importance of generators system control. The control of generators is complex and defficult since they are complex nonlinear plants. Subsequently, this dissertation shows the recent research development in generators control, and selcets several key problems of generators as research objectives to constitute systemic intelligent control theory and methodes. Focusing on these scientific problems, this dissertation is partially supported with the"10th Five Year Plan"Key Technology and Equipment Project-"AC excited hydrogenerator and control system development", and has carried out the following research works:1. A novel parameters identification method for synchronous generators based on Chaotic pattern search algorithms (CPSA) is proposed. The CPSA is a combination of Chaotic optimization algorithms and pattern search algorithms, and it has good search ability and its searching is fast. Parameters identification for generators is treated as a combined optimization of parameters vectors, and an objective function is constructed for this optimization problem. The CPSA method can search the optimal parameters vectors fast, while it is not affected by the nonlinear character and restrictions for disturbance signals are less.2. An approximate model control method for excitation control is presented. Firstly, the outline and mathematic model for excitation system are shown, then an approximate model controller is presented using a direct linearization approach via Taylor expansion, and the control law is realized using RBF neural networks modeling. The robustness of the proposed excitation controller is given.3. An approximate internal model control method for excitation control is studied. Approximate internal model control inclueds approximate model control and feedback compsentation. The approximate model contrller is presented using a direct linearization approach via Taylor expansion, while the feedback compsentation is realized using a rubustness filter. The robustness of the proposed controller is given.4. Turbine governor system is a water, mechanical, electricity integrated plant. An inverse model control method for a rigid water hammer turbine governor is studied for the regulation of non-linear model. First, the principle of the turbine speed regulation system is introduced, and its reversibility is also shown. Secondly, support vector machines is used to identify its inverse model, and two kinds of different inverse model controller are compared: direct inverse model control, inverse model control with PI control Compensation. Direct inverse model control, its design is simple, yet its robustness and interference performance are not as good as inverse model control with PI control compensation.5. A new adaptive inverse model controller for the steam valve is shown in this dissertation. Firstly, the principle and the mathematical model of steam turbine governor is introduced. Secodnly, the irreversibility of the system is shown. Then least-squares supprot vector machines is used to identify the model and the inverse model, and an adaptive learning rate is used for this inverse model control systems. The convergence characteristics of the learning algorithm is analysed.6. A multiple models control system (MMCS) is proposed for excitation and turbine control of synchronous generator. Firstly sub-model control rules for synchronous generator at variable operation points are derived from different operation samples. Then a self-learning algorithms is presented for the online learning of sub-model control rules. In the same time, the sub-model is constructed and the weighting factors for sub-controller are decided by the matching degree between the operation point and the sub-model.7. The integrated control of multi-unit generating system is also presented, and three generating units integrated control system with state-space model is established. The linear quadratic optimal control method is used to design the integrated controller. This dissertation regareds the selection of the weighting matrix of linear quadratic optimal controller as a multi-variable optimization problem, and proposes a parallel chaos optimization algorithms integration with the simplex method to search the best weighting matrix. It is an effective way to reach linear quadratic optimal controller. The simulation results also show the performance of the proposed controller.In the end, the main innovations of the dissertation are summarized, and the fields for further investigation are expected.