本文選題：廣義預(yù)測控制 + 模糊神經(jīng)網(wǎng)絡(luò)��； 參考：《內(nèi)蒙古工業(yè)大學(xué)》2015年碩士論文

【摘要】：燃煤發(fā)電在我國能源結(jié)構(gòu)中占主要地位,盡管有非常規(guī)能源如頁巖氣、可燃冰等出現(xiàn),但目前使用的最廉價(jià)、最安全的能源仍然是煤炭�；痣姀S單元機(jī)組是電網(wǎng)的基本組成部分,保障電網(wǎng)基本的負(fù)荷要求;它擔(dān)負(fù)著電網(wǎng)的調(diào)峰、調(diào)頻任務(wù),影響電網(wǎng)的運(yùn)行穩(wěn)定和經(jīng)濟(jì)效益。在日益嚴(yán)峻的環(huán)境壓力下,如何確保火電廠安全、經(jīng)濟(jì)和高效穩(wěn)定運(yùn)行成為當(dāng)務(wù)之急。機(jī)爐協(xié)調(diào)控制是一個局部線性、全局強(qiáng)非線性,具有雙輸入雙輸出的強(qiáng)耦合控制系統(tǒng)。在全工況范圍內(nèi),采用常規(guī)的控制方法難以保證其得到滿意的控制品質(zhì)。本文是在大量查閱了有關(guān)廣義預(yù)測控制、模糊控制和神經(jīng)網(wǎng)絡(luò)控制文獻(xiàn)的基礎(chǔ)上,針對廣義預(yù)測控制具有在線計(jì)算量大,不適用于非線性系統(tǒng)的特點(diǎn),提出了采用模糊神經(jīng)模型的模糊神經(jīng)網(wǎng)絡(luò)廣義預(yù)測控制。通過采用模糊神經(jīng)網(wǎng)絡(luò)的辨識方法,克服了傳統(tǒng)的模糊系統(tǒng)辨識精度較低的問題,可以有效的解決BP神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法存在收斂速度慢和局部極小值問題;同時該算法有效的減少了廣義預(yù)測控制算法在線計(jì)算量,使廣義預(yù)測控制算法的實(shí)時性得到很大的提高。最后采用300MW單元機(jī)組協(xié)調(diào)控制系統(tǒng)數(shù)學(xué)模型進(jìn)行了仿真研究,仿真結(jié)果表明,控制系統(tǒng)保證對象輸出功率和主蒸汽壓力值能快速平穩(wěn)地跟蹤設(shè)定值。在變負(fù)荷工況下,系統(tǒng)仍然能夠保持良好的控制性能,與傳統(tǒng)PID控制相比該算法表現(xiàn)出良好的自適應(yīng)性和魯棒性。
[Abstract]:Coal-fired power generation plays an important role in China's energy structure. Although unconventional energy sources such as shale gas and combustible ice appear, coal is still the cheapest and safest energy source. The unit of thermal power plant is the basic component of the power network, which guarantees the basic load requirements of the power network, and it undertakes the tasks of peak shaving and frequency modulation of the power network, which affects the stability and economic benefit of the power network. Under the increasingly severe environmental pressure, how to ensure the safety, economy and efficient and stable operation of thermal power plants has become an urgent task. The coordinated control system is a strong coupling control system with local linearity, global strong nonlinearity and double input and double output. In the whole working condition, it is difficult to obtain satisfactory control quality by using conventional control methods. In this paper, on the basis of a large number of literatures on generalized predictive control, fuzzy control and neural network control, the generalized predictive control has the characteristics of large on-line computation and not suitable for nonlinear systems. A fuzzy neural network generalized predictive control based on fuzzy neural model is proposed. By adopting the identification method of fuzzy neural network, the problem of low precision of traditional fuzzy system identification is overcome, and the problem of slow convergence speed and local minimum value of BP neural network learning algorithm can be effectively solved. At the same time, the algorithm effectively reduces the amount of on-line computation of the generalized predictive control algorithm, so that the real-time performance of the generalized predictive control algorithm has been greatly improved. Finally, the mathematical model of coordinated control system of 300MW unit is used to simulate the system. The simulation results show that the control system ensures the output power of the object and the value of the main steam pressure can track the set value quickly and smoothly. Under the variable load condition, the system can still maintain good control performance. Compared with the traditional PID control, the algorithm has good adaptability and robustness.
【學(xué)位授予單位】：內(nèi)蒙古工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM621;TP273

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 王全忠,劉麗梅;多變量控制理論在熱工過程控制中的應(yīng)用綜述[J];電力學(xué)報(bào);1996年01期

2 鄭亞鋒;陳彥橋;衛(wèi)平寶;王維珍;;基于模糊多模型330MW單元機(jī)組協(xié)調(diào)控制系統(tǒng)[J];電力系統(tǒng)及其自動化學(xué)報(bào);2006年02期

3 白雄懷;王文蘭;;燃燒控制系統(tǒng)GPC-PID串級控制策略[J];微計(jì)算機(jī)信息;2009年28期

相關(guān)碩士學(xué)位論文 前2條

1 姜崇鵬;火電廠單元機(jī)組協(xié)調(diào)控制系統(tǒng)的研究[D];蘭州理工大學(xué);2009年

2 禹紅杰;火電機(jī)組協(xié)調(diào)控制系統(tǒng)研究[D];華北電力大學(xué)（北京）;2010年

，

本文編號：1918416