Researches For Synchronous Generator Excitation Control Strategy Based On Hybrid Intelligent Optimization Approach

Synchronous generator excitation control system (SGECS) plays an important role in power system and is associated with the safety, stability and efficient operation of power system. With the continued development of China's economic construction, expanding power systems, large synchronous generator excitation control has become a research focus. And widespread use of fast excitation on SGECS promotes further study of excitation control system. The excitation control system is a time-varying, time-delaying, complex and highly nonlinear control system. When SGECS work condition changes, the dynamic characteristics of the system will significantly change. In this case, the linear PID controller often can not meet the requirements of stability, unless the use of cutting machines, brake failure and other fault treatments. Excitation controller parameter optimization methods are the key to solve the above problems. But unfortunately, the traditional methods of adjusting the controller parameters often use time domain or frequency domain manually setting under human control. Those methods do not have the ability to adjust the control parameters adaptive and lack of constraints on the sudden disturbance signal. Therefore, how to design a simple structure, and has a processing capacity of complex nonlinear excitation control system excitation controller and its parameter optimization method to be the focus of this article.In this paper, taking into account the synchronous generator excitation control system has a complex nonlinear. In a comprehensive analysis based on the characteristics of synchronous generator combination of fuzzy theory and advanced intelligent optimization methods. Systematically and depth study of the SGECS system model, parameter optimization method and control strategy. Further studies of the theory based on nonlinear excitation control system are carrying out and we propose excitation system control strategy based on fuzzy theory and hybrid intelligent optimization. The main research work and innovation achievements are summarized as follows:(1) In considering of characteristics and requirements in large SGECS, the paper establishes a synchronous generator excitation control system model of each part. Especially focus on the relationship about the synchronous generator voltage, current, torque and other energy. We analysis the basic control laws, static and dynamic characteristics of the excitation system. According to research and engineering needs, Corresponding simplification of the theoretical model of SGECS, we access to the practical excitation control system model used by this paper. Those processing provide theoretical supports for the following sections.(2) For complexity and nonlinear in excitation control system, we proposed a nonlinear system parameter optimization strategy which combined with fuzzy theory and the classical PID control law. We design a fuzzy PID excitation controller based on Mamdani fuzzy model after comparative analysis of two fuzzy models. Without considering the accurate modeling of the SGECS system, the new controller can achieve stable operation under multi-operation conditions of the excitation system. Finally, we use comparative experiments to verify the validity of the method.(3) Particle swarm optimization methods in recent years become to be the research focus. By reading a large number of literature references on the analysis of the mechanism of particle swarm optimization and improvements, we introduce two of the most common methods of particle swarm optimization, and on this basis, propose an adaptive particle swarm optimization based on hitting the wall & rebound strategy. A simulation shows the calculation precision and convergence speed of the proposed optimization method in the excitation control system by compared with other two algorithms.(4) For the premature problem of intelligent evolutionary algorithms, we propose to use two steps search strategy to optimize the parameters of excitation system controller by combining chaotic search. Hybrid chaotic PSO method and hybrid chaotic DE method are proposed respectively by researching intelligent optimization algorithm such as particle swarm optimization (PSO) and differential evolution (DE). And use Tent mapping to produce a more uniform chaotic searching solution space. The simulation results and comparative study showed that:the hybrid chaotic and intelligent optimization method has been done the good job between with the standard PSO or DE algorithm. And hybrid chaotic and DE method supplies better performance than hybrid chaotic and PSO method in parameter optimization.