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作 者:徐红伟[1] 李崇晟[1] XU Hongwei;LI Chongsheng(Xi'an Thermal Power Research Institute Co.,Ltd.,Xi'an 710054,China)
机构地区:[1]西安热工研究院有限公司,陕西西安710054
出 处:《热力发电》2020年第4期93-100,共8页Thermal Power Generation
基 金:国家重点研发计划项目(2017YFF0210500)。
摘 要:为实现凝汽式汽轮机的效率衰退研究,避开了传统研究方法中针对排汽焓的计算或预测,通过对汽轮机做功原理的分析,提出了汽轮机效率基准期和汽轮机效率衰退指标2个概念。利用机组历史数据,采用深度信念网络建立了汽轮机效率基准期模型。通过滑动区间的极差限定法对初始数据进行了稳态筛选,并将初始数据与稳态数据相结合实现了模型的训练。通过将汽轮机实时输出功率与基准期模型的理想输出功率的对比,得到了实时汽轮机效率相对基准期的退化程度。应用结果表明:汽轮机效率衰退指标在大修前的螺旋式下降趋势,与汽轮机性能随时间的缓慢下降相吻合;大修后汽轮机效率衰退指标的快速升高,与汽轮机大修后机组通流能力的提升相吻合。该方法可以有效表征汽轮机的效率变化趋势,对电厂人员实时了解汽轮机的经济性提供了有效的参考。In order to research the efficiency degradation of condensing steam turbines, the calculation and prediction of exhaust enthalpy in conventional research methods is avoided, and two concepts, namely the efficiency reference period and the efficiency degradation index, are put forward, by analyzing the working principle of steam turbines. Based on historical data, a model of the turbine in efficiency reference period is established by deep belief network. The initial data are filtered steadily by limiting the range of data in continuous sliding intervals, and the model is trained by the initial data combined with the steady data. By comparing the real-time output power of the turbine with the ideal output power of the model, the degradation degree of the real-time turbine efficiency relative to the reference period is obtained. The application results show that, the spiral decline trend of turbine efficiency degradation index before overhaul is consistent with the slow decline of turbine performance over time, and the rapid rise of turbine efficiency degradation index after overhaul is consistent with the increase of turbine current capability after overhaul. This method can effectively characterize the efficiency trend of the turbine, and provide an effective reference for users to master the performance of the turbine in real time.
关 键 词:凝汽式汽轮机 效率衰退 效率基准期 深度信念网络 受限玻尔兹曼机 神经网络
分 类 号:TK267[动力工程及工程热物理—动力机械及工程]
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