發(fā)布時(shí)間：2018-03-31 06:39

  本文選題：目標(biāo)模式識(shí)別　切入點(diǎn)：非平穩(wěn)過程　出處：《華北電力大學(xué)》2017年碩士論文

【摘要】：目標(biāo)模式識(shí)別在工業(yè)設(shè)備及系統(tǒng)狀態(tài)監(jiān)測(cè)、故障診斷、優(yōu)化控制、協(xié)同調(diào)度等方面獲得廣泛應(yīng)用。但常用如主元分析、聚類分析、證據(jù)理論、支持向量機(jī)等方法本質(zhì)上都立足于時(shí)間域,只能對(duì)離散域內(nèi)的信息進(jìn)行處理,僅可實(shí)現(xiàn)平穩(wěn)過程的目標(biāo)模式識(shí)別�，F(xiàn)場(chǎng)存在大量非平穩(wěn)過程的目標(biāo)模式識(shí)別問題,直接應(yīng)用以上方法分析時(shí)域內(nèi)信號(hào)時(shí),則會(huì)因信號(hào)動(dòng)態(tài)時(shí)間上的差異導(dǎo)致分析結(jié)果出現(xiàn)巨大誤差,容易造成誤判和漏判。受電網(wǎng)負(fù)荷指令頻繁變化、煤質(zhì)波動(dòng)加劇等因素影響,火電機(jī)組動(dòng)態(tài)變工況運(yùn)行所占時(shí)間的比例大大增加。課題研究思路為,將時(shí)域內(nèi)信號(hào)處理方法與離散域內(nèi)信息融合方法相結(jié)合,實(shí)現(xiàn)對(duì)動(dòng)態(tài)過程的目標(biāo)模式識(shí)別。并分別進(jìn)行了以下嘗試:(1)將能夠反映信號(hào)變化特征的信息熵理論應(yīng)用于機(jī)組AGC(自動(dòng)發(fā)電控制)指令模式識(shí)別方面;(2)采用多尺度濾波器對(duì)變化速度不一致的信號(hào)進(jìn)行分解,選擇相同的時(shí)間尺度下的信號(hào)進(jìn)行D-S融合處理,并將此方法應(yīng)用于中速磨煤機(jī)“臨界堵塞”故障判斷方面;(3)利用軟測(cè)量技術(shù)構(gòu)造出靜態(tài)精度好和動(dòng)態(tài)響應(yīng)速度快的中間信號(hào)并進(jìn)行動(dòng)態(tài)補(bǔ)償,保證原始信號(hào)時(shí)間尺度一致后再進(jìn)行判斷,并將此方法應(yīng)用于供熱機(jī)組供熱季內(nèi)安全運(yùn)行模式識(shí)別方面。經(jīng)過機(jī)理分析、仿真分析、現(xiàn)場(chǎng)運(yùn)行數(shù)據(jù)分析和實(shí)際調(diào)試后,應(yīng)用結(jié)果表明此類方法能夠有針對(duì)性的解決一些現(xiàn)場(chǎng)問題,獲得了較好的實(shí)際效果。課題研究在非平穩(wěn)過程的目標(biāo)模式識(shí)別方面作出了有益的探索。
[Abstract]:Target pattern recognition has been widely used in industrial equipment and system condition monitoring, fault diagnosis, optimal control, cooperative scheduling and so on. However, it is commonly used such as principal component analysis, cluster analysis, evidence theory, etc. Support vector machine (SVM) is essentially based on time domain. It can only process the information in discrete domain and can only realize the target pattern recognition of stationary process. There are a lot of non-stationary process target pattern recognition problems in the field. When the above method is used directly to analyze the signals in the time domain, there will be huge errors in the analysis results due to the difference in the dynamic time of the signals, which will easily lead to misjudgment and missing judgment, which will be affected by the frequent changes of power network load orders and the aggravation of coal quality fluctuations. The proportion of time occupied by the dynamic off-condition operation of thermal power units is greatly increased. The research idea is to combine the signal processing method in time domain with the information fusion method in discrete domain. The target pattern recognition of dynamic process is realized. The following attempts are made: 1) the information entropy theory, which can reflect the characteristics of signal variation, is applied to the pattern recognition of AGC (automatic Generation Control) instruction of generating set. (2) Multi-scale is adopted. The filter decomposes the signal with inconsistent speed. D-S fusion processing is performed by selecting signals of the same time scale. The method is applied to the judgment of "critical blockage" fault of medium speed coal mill. The intermediate signal with good static precision and fast dynamic response is constructed by soft sensor technology and dynamically compensated. The method is applied to the safe operation pattern recognition of heating units in the heating season. After mechanism analysis, simulation analysis, field operation data analysis and actual debugging, The application results show that this method can solve some field problems and obtain good practical results.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.4;TM621

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