【摘要】：汽輪機抽汽回熱系統(tǒng)節(jié)能優(yōu)化的核心和難點在于抽汽回熱系統(tǒng)能效指標基準狀態(tài)的確定。復雜多變的邊界條件以及能效指標間的耦合問題給汽輪機抽汽回熱系統(tǒng)的節(jié)能優(yōu)化研究帶來了很大的挑戰(zhàn)。目前,汽輪機抽汽回熱系統(tǒng)關鍵能效指標的基準值確定往往僅僅采用設計值、變工況計算值或者熱力試驗值,每種基準值確定方法都有其局限性。隨著機組運行工況變化和設備性能狀態(tài)的改變,基準值已無法匹配抽汽回熱系統(tǒng)的實際運行狀態(tài),使得運行指導受到很大限制,無法發(fā)現(xiàn)引起能效水平降低的真正原因。基于汽輪機抽汽回熱系統(tǒng)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組的實際運行狀態(tài),因此能夠很好地確定目標工況下抽汽回熱系統(tǒng)實際可達的能效指標基準狀態(tài)。針對目前抽汽回熱系統(tǒng)數(shù)據(jù)挖掘中面臨的多變復雜的邊界條件,多且耦合的能效指標以及指標差異的問題。本文通過基于k-means聚類的數(shù)據(jù)挖掘方式提取出目標工況下抽汽回熱系統(tǒng)實際可達的能效指標基準狀態(tài)。但是基于數(shù)據(jù)挖掘得到的能效指標基準狀態(tài)受到運行邊界條件和實際設備狀態(tài)的影響,主要反映的是操作人員運行水平的高低,而沒有反映出目標工況下設備性能的基準狀態(tài)。因此,本文結合抽汽回熱系統(tǒng)實際情況通過進一步構建設備性能類指標的基準狀態(tài)模型,對挖掘得到的反映設備性能的能效指標進行修正,從而得到整個汽輪機抽汽回熱系統(tǒng)能效指標實際可達的基準狀態(tài),完成了關鍵能效指標的耗差因子分析,同時采用基于機理與Ebislon仿真建模的方法完成了端差及給水溫度的基準值及耗差因子的驗證,從而為汽輪機抽汽回熱系統(tǒng)不同工況的能耗分析與能效診斷提供依據(jù)。最后,本文基于抽汽回熱系統(tǒng)能效指標基準狀態(tài)的研究,對抽汽回熱系統(tǒng)能效分析、評價與診斷系統(tǒng)展開了設計研究工作。對某1000MW汽輪機回熱系統(tǒng)組通過耗差因子分析找到影響該抽汽回熱系統(tǒng)能耗的主要能效指標,基于能效指標的優(yōu)化知識庫,指導能效指標的優(yōu)化調(diào)整,最終達到提高抽汽回熱系統(tǒng)能效水平的目的。
[Abstract]:The core and difficulty of energy saving optimization of steam turbine extraction recuperation system lies in the determination of the benchmark state of energy efficiency index of extraction steam recovery system. The complex boundary conditions and the coupling problem between energy efficiency indexes have brought great challenges to the optimization of energy conservation of steam turbine extraction heat recovery system. At present, the reference value of the key energy efficiency index of steam turbine extraction recuperation system is usually determined only by the design value, the calculation value under off-condition or the thermal test value, and each method has its limitations. With the change of unit operating condition and equipment performance state, the reference value can not match the actual operation state of the extraction steam recovery system, so the operation guidance is greatly restricted, and the real cause of the reduction of energy efficiency level can not be found. The data mining method based on the massive historical data of steam turbine extraction recuperation system can well match the actual operating state of the unit, so it can determine the actual energy efficiency index reference state of the extraction regenerative system under the target working condition. In order to solve the problem of variable and complex boundary conditions, multiple coupled energy efficiency indexes and different indexes in data mining of extraction steam recuperation system. In this paper, the data mining method based on k-means clustering is used to extract the actual energy efficiency standard state of the extraction regenerative system under the target working condition. But the datum state of energy efficiency index based on data mining is affected by the operation boundary condition and the actual equipment state, which mainly reflects the operating level of the operator, but does not reflect the standard state of the equipment performance under the target working condition. Therefore, according to the actual situation of the extraction heat recovery system, this paper modifies the energy efficiency index which can reflect the equipment performance by further constructing the benchmark state model of the equipment performance index. Thus, the reference state of the energy efficiency index of the whole steam turbine recovery system is obtained, and the consumption difference factor analysis of the key energy efficiency index is completed. At the same time, based on mechanism and Ebislon simulation modeling method, the standard value and consumption factor of end difference and feed water temperature are verified, which provides the basis for energy consumption analysis and energy efficiency diagnosis of steam turbine recovery system under different working conditions. Finally, based on the research of the energy efficiency benchmark state of the extraction steam recovery system, the energy efficiency analysis, evaluation and diagnosis system of the extraction steam recovery system are designed and studied. The main energy efficiency indexes affecting the energy consumption of the recovery system of a 1000MW steam turbine are found through the analysis of the consumption difference factor. The optimization knowledge base based on the energy efficiency index is used to guide the optimization and adjustment of the energy efficiency index. Finally, the purpose of improving the energy efficiency of the extraction steam recuperator system is achieved.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM621.3

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