汽轮机组能效优化策略的研究

发布时间：2018-03-13 12:38

本文选题：汽轮机组　切入点：敏度分析　出处：《华北电力大学(北京)》2016年硕士论文　论文类型：学位论文

【摘要】：复杂多变的边界条件以及能效指标间的耦合问题给火电机组的节能优化研究带来了很大的挑战。火电机组节能优化的核心和难点在于火电机组能效指标基准状态的确定。目前,火电机组关键能效指标的基准值确定往往仅仅采用机组设计值、变工况计算值或者热力试验值,每种基准值确定方法都有其局限性。随着机组运行工况变化和设备性能状态的改变,基准值己无法匹配机组的实际运行状态,使得运行指导受到很大限制,无法发现引起能效水平降低的真正原因。基于汽轮机组海量历史数据的数据挖掘方法能够较好地匹配机组的实际状态,因此能够很好地确定目标工况下机组实际可达的能效指标基准状态。针对目前机组数据挖掘中面临的多变复杂的边界条件,多且耦合的能效指标以及指标差异的问题。本文通过基于模糊C均值聚类,灰色关联约简和主观AHP层次分析法与客观熵权法相结合的组合权重法的数据挖掘方式提取出目标工况下机组实际可达的能效指标基准状态。但是基于数据挖掘得到的能效指标基准状态受到运行边界条件和实际设备状态的影响,主要反映的是操作人员运行水平的高低,而没有反映出目标工况下设备性能的基准状态。因此,本文结合机组实际情况通过进一步构建设备性能类指标的基准状态模型,对挖掘得到的反映设备性能的能效指标进行修正,从而得到整个汽轮机组能效指标实际可达的基准状态,并且分析验证了模型的准确性,从而为汽轮机组不同工况的能耗分析与节能诊断提供依据。最后,本文基于汽轮机组能效指标基准状态的研究,对汽轮机系统能效优化模块展开了设计研究工作。对某600MW汽轮机组通过敏度分析找到影响该机组能耗的主要能效指标,基于能效指标的优化知识库,指导能效指标的优化调整,最终达到提高机组能效水平的目的。
[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.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM621;TK26

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