本文選題：超臨界機(jī)組 + 協(xié)調(diào)控制系統(tǒng)��； 參考：《華北電力大學(xué)》2017年碩士論文

【摘要】：超臨界機(jī)組因其具有節(jié)能、高效等優(yōu)點(diǎn),現(xiàn)已成為我國電網(wǎng)的主力機(jī)組,其最主要的任務(wù)是滿足電網(wǎng)負(fù)荷需求,接受自動(dòng)發(fā)電控制(AGC)參與電網(wǎng)的調(diào)峰和調(diào)頻。因?yàn)槌R界機(jī)組為多輸入多輸出的非線性、強(qiáng)耦合的被控對象,傳統(tǒng)的PID控制,已經(jīng)無法達(dá)到機(jī)組深度調(diào)峰的要求,使機(jī)組在大幅度變工況運(yùn)行時(shí)的控制效果變差,因此引入先進(jìn)的智能控制策略來提高機(jī)組的控制品質(zhì)十分必要。本文主要對新型的螢火蟲算法(FA)進(jìn)行研究和改進(jìn),并與成熟的粒子群算法性能進(jìn)行比較,并將FA算法與神經(jīng)網(wǎng)絡(luò)建模結(jié)合應(yīng)用于超臨界機(jī)組的協(xié)調(diào)預(yù)測優(yōu)化控制,選題具有理論和應(yīng)用兩個(gè)方面的重要意義。本文針對某600MW超臨界機(jī)組,詳細(xì)分析了其協(xié)調(diào)系統(tǒng)的各種特性以及控制方式和控制邏輯。在研究神經(jīng)網(wǎng)絡(luò)的原理以及非線性系統(tǒng)建模方法、對FA算法及PSO算法性能比較研究的基礎(chǔ)上,提出了一種基于BP神經(jīng)網(wǎng)絡(luò)建模和基于混沌序列螢火蟲算法(CSFA)的模型預(yù)測優(yōu)化控制(MPOC)方法,并應(yīng)用于超臨界機(jī)組協(xié)調(diào)控制。本文采用MATLAB軟件平臺(tái)建立了協(xié)調(diào)預(yù)測優(yōu)化控制算法,通過與超臨界機(jī)組全范圍仿真系統(tǒng)進(jìn)行雙向?qū)崟r(shí)通訊,對600MW超臨界機(jī)組進(jìn)行實(shí)時(shí)優(yōu)化控制,開展詳細(xì)的協(xié)調(diào)優(yōu)化控制仿真試驗(yàn)。結(jié)果表明:本文提出的方法能夠有效地提高機(jī)組對負(fù)荷指令的響應(yīng)速度和調(diào)節(jié)精度,大大減小了主蒸汽壓力的控制偏差,使其能夠在控制要求內(nèi),保證機(jī)組的運(yùn)行安全和經(jīng)濟(jì)效益,具有較好的工程實(shí)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.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TM621

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本文編號：2089981