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PARTIAL LEAST SQUARES FOR POWER PLANT PERFORMANCE MONITORING

机译:电厂性能监测的最小二乘平方

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摘要

The problems faced in monitoring power plant performance are outlined and consideration is given to more effective utilization of the data generated on-line by the distributed control system (DCS). Partial least squares (PLS) is identified as a method ideally suited to coping with the large number of correlated and collinear signals available on a power plant, for process monitoring and performance related analysis. Non-linear PLS models are trained using archived data from a local utility to predict quality measures of thermal efficiency, and NO_x and SO_x emissions for generating units dual-firing on both oil and gas. The performance and diagnostic capabilities of the resulting models are examined, illustrating the simplicity of operation of the proposed monitoring tool.
机译:概述了监视电厂性能时遇到的问题,并考虑了更有效地利用分布式控制系统(DCS)在线生成的数据。偏最小二乘(PLS)被认为是一种理想的方法,适合处理发电厂中可用的大量相关和共线信号,以进行过程监控和与性能相关的分析。非线性PLS模型使用来自本地公用事业的存档数据进行训练,以预测热效率以及发电机组在石油和天然气上双点火的NO_x和SO_x排放的质量度量。检查了所得模型的性能和诊断能力,从而说明了所提出的监视工具的操作简便性。

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