本文關鍵詞： 同步發(fā)電機 勵磁控制 PID 模糊理論 粒子群優(yōu)化算法　出處：《重慶交通大學》2014年碩士論文　論文類型：學位論文

【摘要】：本文針對同步發(fā)電機勵磁控制系統(tǒng)所具有的復雜非線性的特點，同時在全面分析了同步發(fā)電機特性的基礎上，結合模糊理論知識及先進的智能優(yōu)化控制方法，對發(fā)電機組勵磁控制系統(tǒng)進行了仿真建模、并對其參數(shù)的優(yōu)化方法及系統(tǒng)控制策略進行了深入系統(tǒng)的研究，進一步開展了基于非線性勵磁控制系統(tǒng)的理論研究，提出了基于粒子群優(yōu)化算法的模糊自適應智能優(yōu)化方法的勵磁控制策略體系。 本文首先針對大型的同步發(fā)電機特性及其勵磁系統(tǒng)進行深入研究，在全面分析了勵磁系統(tǒng)需求后，建立了同步發(fā)電機勵磁控制系統(tǒng)各環(huán)節(jié)的數(shù)學模型，同時分析了勵磁系統(tǒng)的基本控制規(guī)律及其靜、動態(tài)特性。根據(jù)研究與工程的需求，對理論模型進行了相應簡化得到本文仿真用的實用勵磁控制系統(tǒng)傳遞參數(shù)模型，為后面章節(jié)研究提供理論支持。 粒子群優(yōu)化是近年來智能優(yōu)化方法中的研究熱點。本文首先深入分析了粒子群算法的機理基礎，并對算法的重要因子進行了算法優(yōu)化，并完整記錄優(yōu)化結果，通過分析粒子篩選過程，肯定了優(yōu)化算法的正確性，，在此基礎上，提出了一種自適應粒子群優(yōu)化算法。通過在勵磁系統(tǒng)中對算法的應用，得到實例仿真結果，通過分析比較了該算法與另外常見粒子群算法在勵磁系統(tǒng)控制中的計算精度和收斂速度。 針對勵磁控制系統(tǒng)的復雜非線性特性，結合模糊控制方法對非線性系統(tǒng)的控制效果，將其理論應用于傳統(tǒng)的PID控制規(guī)律，提出一種非線性系統(tǒng)參數(shù)優(yōu)化策略。在現(xiàn)有的模糊模型的基礎上，設計出一種基于Mamdani模糊模型的模糊PID勵磁控制器，并通過對模糊控制器各種可調(diào)參數(shù)的對比實驗得出最優(yōu)的控制器設計方案。該模糊控制器能夠在不考慮系統(tǒng)精確建模的情況下，實現(xiàn)多工況下勵磁系統(tǒng)的穩(wěn)定控制。最后經(jīng)過對比實驗，驗證了該方法的有效性。 最后，結合模糊控制在非線性系統(tǒng)控制上的優(yōu)勢及粒子群優(yōu)化算法在對參數(shù)優(yōu)化上的優(yōu)勢，提出了模糊自適應PID勵磁控制的智能控制策略。大致的方案是先通過PSO算法選出系統(tǒng)的初始參數(shù)，然后利用FAPID對系統(tǒng)進行動態(tài)控制。將這種策略控制簡單、精度高等優(yōu)點與勵磁控制規(guī)則結合靈活、快速反應系統(tǒng)的動態(tài)變化并及時讓系統(tǒng)重新達到穩(wěn)定狀態(tài)。
[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.
【學位授予單位】：重慶交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM31;TP18

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