同步发电机励磁系统的智能控制方法研究

发布时间：2018-03-01 03:14

本文关键词： 同步发电机励磁控制 PID 模糊理论粒子群优化算法　出处：《重庆交通大学》2014年硕士论文　论文类型：学位论文

【摘要】：本文针对同步发电机励磁控制系统所具有的复杂非线性的特点，同时在全面分析了同步发电机特性的基础上，结合模糊理论知识及先进的智能优化控制方法，对发电机组励磁控制系统进行了仿真建模、并对其参数的优化方法及系统控制策略进行了深入系统的研究，进一步开展了基于非线性励磁控制系统的理论研究，提出了基于粒子群优化算法的模糊自适应智能优化方法的励磁控制策略体系。本文首先针对大型的同步发电机特性及其励磁系统进行深入研究，在全面分析了励磁系统需求后，建立了同步发电机励磁控制系统各环节的数学模型，同时分析了励磁系统的基本控制规律及其静、动态特性。根据研究与工程的需求，对理论模型进行了相应简化得到本文仿真用的实用励磁控制系统传递参数模型，为后面章节研究提供理论支持。粒子群优化是近年来智能优化方法中的研究热点。本文首先深入分析了粒子群算法的机理基础，并对算法的重要因子进行了算法优化，并完整记录优化结果，通过分析粒子筛选过程，肯定了优化算法的正确性，，在此基础上，提出了一种自适应粒子群优化算法。通过在励磁系统中对算法的应用，得到实例仿真结果，通过分析比较了该算法与另外常见粒子群算法在励磁系统控制中的计算精度和收敛速度。针对励磁控制系统的复杂非线性特性，结合模糊控制方法对非线性系统的控制效果，将其理论应用于传统的PID控制规律，提出一种非线性系统参数优化策略。在现有的模糊模型的基础上，设计出一种基于Mamdani模糊模型的模糊PID励磁控制器，并通过对模糊控制器各种可调参数的对比实验得出最优的控制器设计方案。该模糊控制器能够在不考虑系统精确建模的情况下，实现多工况下励磁系统的稳定控制。最后经过对比实验，验证了该方法的有效性。最后，结合模糊控制在非线性系统控制上的优势及粒子群优化算法在对参数优化上的优势，提出了模糊自适应PID励磁控制的智能控制策略。大致的方案是先通过PSO算法选出系统的初始参数，然后利用FAPID对系统进行动态控制。将这种策略控制简单、精度高等优点与励磁控制规则结合灵活、快速反应系统的动态变化并及时让系统重新达到稳定状态。
[Abstract]:Aiming at the complex and nonlinear characteristics of synchronous generator excitation control system, this paper analyzes the characteristics of synchronous generator, combines fuzzy theory knowledge and advanced intelligent optimal control method. The excitation control system of generator set is simulated and modeled, and the optimization method and control strategy of the excitation control system are studied systematically, and the theoretical research based on the nonlinear excitation control system is carried out. The excitation control strategy system of fuzzy adaptive intelligent optimization method based on particle swarm optimization (PSO) is proposed. In this paper, the characteristics of large synchronous generator and its excitation system are studied deeply. After analyzing the requirement of excitation system, the mathematical model of excitation control system of synchronous generator is established. At the same time, the basic control law and static and dynamic characteristics of excitation system are analyzed. According to the needs of research and engineering, the theoretical model is simplified to obtain the practical excitation control system transfer parameter model used in this paper. To provide theoretical support for later chapters. Particle swarm optimization (PSO) is a hot topic in intelligent optimization methods in recent years. Firstly, the mechanism of PSO is deeply analyzed, and the important factors of PSO are optimized, and the optimization results are recorded. By analyzing the process of particle selection, the correctness of the optimization algorithm is confirmed. On the basis of this, an adaptive particle swarm optimization algorithm is proposed. The simulation results are obtained by the application of the algorithm in the excitation system. The computational accuracy and convergence rate of this algorithm and other common particle swarm optimization algorithms in excitation system control are analyzed and compared. In view of the complex nonlinear characteristics of excitation control system, combined with the control effect of fuzzy control method for nonlinear system, its theory is applied to the traditional PID control law. Based on the existing fuzzy model, a fuzzy PID excitation controller based on Mamdani fuzzy model is designed. The optimal controller design scheme is obtained by comparing various adjustable parameters of the fuzzy controller. The fuzzy controller can be designed without considering the precise modeling of the system. Finally, the effectiveness of the method is verified by a comparative experiment. Finally, combining the advantages of fuzzy control in nonlinear system control and particle swarm optimization algorithm in parameter optimization, This paper presents an intelligent control strategy for fuzzy adaptive PID excitation control. The general scheme is to select the initial parameters of the system through the PSO algorithm, and then use FAPID to control the system dynamically. With high precision and flexible excitation control rules, the dynamic change of the system can be quickly reacted and the system can be restored to a stable state in time.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM31;TP18

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