FA和PSO算法比较研究及其在协调优化中的应用

发布时间：2018-07-02 11:33

本文选题：超临界机组 + 协调控制系统　；参考：《华北电力大学》2017年硕士论文

【摘要】：超临界机组因其具有节能、高效等优点,现已成为我国电网的主力机组,其最主要的任务是满足电网负荷需求,接受自动发电控制(AGC)参与电网的调峰和调频。因为超临界机组为多输入多输出的非线性、强耦合的被控对象,传统的PID控制,已经无法达到机组深度调峰的要求,使机组在大幅度变工况运行时的控制效果变差,因此引入先进的智能控制策略来提高机组的控制品质十分必要。本文主要对新型的萤火虫算法(FA)进行研究和改进,并与成熟的粒子群算法性能进行比较,并将FA算法与神经网络建模结合应用于超临界机组的协调预测优化控制,选题具有理论和应用两个方面的重要意义。本文针对某600MW超临界机组,详细分析了其协调系统的各种特性以及控制方式和控制逻辑。在研究神经网络的原理以及非线性系统建模方法、对FA算法及PSO算法性能比较研究的基础上,提出了一种基于BP神经网络建模和基于混沌序列萤火虫算法(CSFA)的模型预测优化控制(MPOC)方法,并应用于超临界机组协调控制。本文采用MATLAB软件平台建立了协调预测优化控制算法,通过与超临界机组全范围仿真系统进行双向实时通讯,对600MW超临界机组进行实时优化控制,开展详细的协调优化控制仿真试验。结果表明:本文提出的方法能够有效地提高机组对负荷指令的响应速度和调节精度,大大减小了主蒸汽压力的控制偏差,使其能够在控制要求内,保证机组的运行安全和经济效益,具有较好的工程实用性。
[Abstract]:Because of its advantages of energy saving and high efficiency, supercritical unit has become the main power unit in our country. Its main task is to meet the demand of power grid load and to accept automatic generation control (AGC) to participate in peak shaving and frequency modulation. Because the supercritical unit is a nonlinear, strong coupling controlled object with multiple inputs and outputs, the traditional pid control can no longer meet the requirements of the unit's deep peak-shaving, which makes the control effect of the unit worse when the unit is running in a large range of variable operating conditions. Therefore, it is necessary to introduce advanced intelligent control strategy to improve the control quality of the unit. In this paper, a new type of firefly algorithm (FA) is studied and improved, and its performance is compared with the mature particle swarm optimization algorithm (PSO). The FA algorithm and neural network modeling are applied to the coordinated predictive optimization control of supercritical units. The selection of topics is of great significance in both theory and application. In this paper, for a 600MW supercritical unit, the characteristics, control mode and control logic of the coordination system are analyzed in detail. On the basis of studying the principle of neural network and the modeling method of nonlinear system, the performance of FA algorithm and PSO algorithm are compared. A model predictive optimization control (MPOC) method based on BP neural network modeling and chaotic sequence firefly algorithm (CSFA) is proposed and applied to supercritical unit coordination control. In this paper, a coordinated predictive optimal control algorithm is established by using MATLAB software platform. Through two-way real-time communication with the full-range simulation system of supercritical unit, the real-time optimal control of 600MW supercritical unit is carried out. Carry out detailed simulation test of coordinated optimal control. The results show that the proposed method can effectively improve the response speed and adjustment accuracy of the unit to the load command, greatly reduce the control deviation of the main steam pressure, and enable it to meet the control requirements. It has good engineering practicability to ensure the operation safety and economic benefit of the unit.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TM621

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