Study On Adaptive Variable Structure Control For Power System Transient Stability

The state power systems are now entering a new era of power energy which with large power unit and with special high voltage, and power energy be transported by hybrid alternative current and direct current. The running and controlling of power system are becoming more and more complicated. How to control special large power systems is facing a serious challenge. Power system is of a typical nonlinear dynamics of multi-dimension and with strong nonlinearity and uncertainty and in which much intricate phenomena and movement law still unknown. In order to ensure power systems running credible stable for long period and obtain suitable and effective control approach, the study on the control theory and control practice must be further intensified. Facing on the fast change and complicated power systems, the ability of traditional exacting control method with fixed structure will be limited for improving power system stability. For the last decades, the control of power systems transient stability especially excitation control methods had be investigated widely and deeply by electricity specialists of world. Many effective control strategies and methods of the practices and theory be proposed, but still a lot of problems remain to unresolved. This thesis focused on the study of the power system transient control and especially on the excitation control with uncertainty based on prior knowledge and investigating the modern adaptive variable structure control strategy for uncertain power system transient stability. The following control methods and control strategies are presented in this thesis.(1) Summarizing the basic definition of power system transient stability and describing the relationship between the mechanical and electromagnetism briefly, and then review the developments of control strategies of power system transient stability. Finally, the progress of control methods of power system transient control is introduced.(2) Power system is of a typical dynamics systems with strong nonlinearity, non-autonomous and high-dimensions. It is a difficult task for analyzing and controlling and the study on it should be guided by suitable theory and necessary control tools. So it is necessary for providing background of stability theory. Lyapunov stability theory and tools of stability analyzing is introduced into and at the same time Barbalat lemma is provided. For a class of uncertain nonlinear systems with parameter can linearization, the design procedure of the controller by using backstepping methods is presented and also explaining the principle of control invariable of the sliding mode control. The time of the sliding to the balance point of the fast terminal system is calculated. The conception and control mode of neural network control is introduced.(3) A adaptive fast terminal sliding excitation controller for SMIB power system transient stability is proposed based on combining with fast terminal sliding mode control, backstepping and adaptive control. Simulation performed on SMIB shows that the proposed controller have good properties of adaptive and suppress disturbances and also shows good control performances.(4) A multi-sliding mode adaptive excitation controller is proposed for multi-machine power systems. The mismatched problem of parameter and disturbances for multi-machines power systems is solved by using disturbance observers. In order to overcome the chattering of input signal of sliding mode controller, a smooth S-type function which can infinitive differential is introduced to substitute for switching function. Simulation performed on 6-machine 22-nodes systems shows that the proposed controller have good properties of attenuating disturbances and can improve power systems transient stability effectively.(5) Considering the difficulty for exacting modeling of power system transient stability control, radial base function neural network is introduced to approximate the uncertainty and external disturbances. For a class of nonlinear uncertain system, adaptive control law, weight update law, parameter update law of the system in general meaning are given. By applying the above conclusion into SMIB, a adaptive excitation controller based on neural network control is proposed. Simulation studies are included to illustrate the effectiveness of the proposed controller.