本文選題：汽輪機(jī)組　切入點(diǎn)：敏度分析　出處：《華北電力大學(xué)(北京)》2016年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：復(fù)雜多變的邊界條件以及能效指標(biāo)間的耦合問(wèn)題給火電機(jī)組的節(jié)能優(yōu)化研究帶來(lái)了很大的挑戰(zhàn)。火電機(jī)組節(jié)能優(yōu)化的核心和難點(diǎn)在于火電機(jī)組能效指標(biāo)基準(zhǔn)狀態(tài)的確定。目前,火電機(jī)組關(guān)鍵能效指標(biāo)的基準(zhǔn)值確定往往僅僅采用機(jī)組設(shè)計(jì)值、變工況計(jì)算值或者熱力試驗(yàn)值,每種基準(zhǔn)值確定方法都有其局限性。隨著機(jī)組運(yùn)行工況變化和設(shè)備性能狀態(tài)的改變,基準(zhǔn)值己無(wú)法匹配機(jī)組的實(shí)際運(yùn)行狀態(tài),使得運(yùn)行指導(dǎo)受到很大限制,無(wú)法發(fā)現(xiàn)引起能效水平降低的真正原因�；谄啓C(jī)組海量歷史數(shù)據(jù)的數(shù)據(jù)挖掘方法能夠較好地匹配機(jī)組的實(shí)際狀態(tài),因此能夠很好地確定目標(biāo)工況下機(jī)組實(shí)際可達(dá)的能效指標(biāo)基準(zhǔn)狀態(tài)。針對(duì)目前機(jī)組數(shù)據(jù)挖掘中面臨的多變復(fù)雜的邊界條件,多且耦合的能效指標(biāo)以及指標(biāo)差異的問(wèn)題。本文通過(guò)基于模糊C均值聚類(lèi),灰色關(guān)聯(lián)約簡(jiǎn)和主觀AHP層次分析法與客觀熵權(quán)法相結(jié)合的組合權(quán)重法的數(shù)據(jù)挖掘方式提取出目標(biāo)工況下機(jī)組實(shí)際可達(dá)的能效指標(biāo)基準(zhǔn)狀態(tài)。但是基于數(shù)據(jù)挖掘得到的能效指標(biāo)基準(zhǔn)狀態(tài)受到運(yùn)行邊界條件和實(shí)際設(shè)備狀態(tài)的影響,主要反映的是操作人員運(yùn)行水平的高低,而沒(méi)有反映出目標(biāo)工況下設(shè)備性能的基準(zhǔn)狀態(tài)。因此,本文結(jié)合機(jī)組實(shí)際情況通過(guò)進(jìn)一步構(gòu)建設(shè)備性能類(lèi)指標(biāo)的基準(zhǔn)狀態(tài)模型,對(duì)挖掘得到的反映設(shè)備性能的能效指標(biāo)進(jìn)行修正,從而得到整個(gè)汽輪機(jī)組能效指標(biāo)實(shí)際可達(dá)的基準(zhǔn)狀態(tài),并且分析驗(yàn)證了模型的準(zhǔn)確性,從而為汽輪機(jī)組不同工況的能耗分析與節(jié)能診斷提供依據(jù)。最后,本文基于汽輪機(jī)組能效指標(biāo)基準(zhǔn)狀態(tài)的研究,對(duì)汽輪機(jī)系統(tǒng)能效優(yōu)化模塊展開(kāi)了設(shè)計(jì)研究工作。對(duì)某600MW汽輪機(jī)組通過(guò)敏度分析找到影響該機(jī)組能耗的主要能效指標(biāo),基于能效指標(biāo)的優(yōu)化知識(shí)庫(kù),指導(dǎo)能效指標(biāo)的優(yōu)化調(diào)整,最終達(dá)到提高機(jī)組能效水平的目的。
[Abstract]:The complex boundary conditions and the coupling problem between energy efficiency indexes bring great challenges to the energy conservation optimization of thermal power units. The core and difficulty of energy efficiency optimization of thermal power units lies in the determination of the benchmark state of energy efficiency index of thermal power units. The benchmark value of key energy efficiency index of thermal power unit is usually determined only by unit design value, variable working condition calculation value or thermal test value. With the change of unit operating condition and equipment performance state, the reference value can not match the actual operation state of the unit, so the operation guidance is greatly restricted. The real cause of the decrease in energy efficiency can not be found. The data mining method based on the massive historical data of steam turbine units can better match the actual status of the unit. Therefore, the benchmark state of the actual energy efficiency index of the unit can be determined very well under the target working condition. In view of the changeable and complex boundary conditions in the data mining of the unit at present, In this paper, based on fuzzy C-means clustering, the problem of multiple and coupled energy efficiency indexes and their differences is discussed. The data mining method of combined weight method, which combines grey relational reduction and subjective AHP analytic hierarchy process with objective entropy weight method, extracts the actual energy efficiency index datum state of the unit under the target working condition. However, based on the data mining, the results are obtained. The baseline state of the energy efficiency indicator to be reached is affected by the operating boundary conditions and the actual equipment state, It mainly reflects the operating level of the operator, but does not reflect the reference state of the equipment performance under the target operating condition. Therefore, this paper constructs the benchmark state model of the equipment performance class index by further constructing the reference state model according to the actual conditions of the unit. The energy efficiency index which reflects the performance of the equipment is modified, and the actual reference state of the energy efficiency index of the whole turbine unit is obtained, and the accuracy of the model is analyzed and verified. So as to provide the basis for energy consumption analysis and energy saving diagnosis of steam turbine unit under different working conditions. Finally, based on the research of energy efficiency index reference state of steam turbine unit, The energy efficiency optimization module of steam turbine system is designed and studied. The main energy efficiency indexes affecting the energy consumption of a 600MW steam turbine unit are found through sensitivity analysis, and the optimization knowledge base based on energy efficiency index is used to guide the optimization and adjustment of energy efficiency index. Finally, the purpose of improving the energy efficiency level of the unit is achieved.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TM621;TK26

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