K-NN回归在煤机相关变量预测中的应用

Vedika Agrawal, Shubham Agrawal, Sayak Nag, D. Chakraborty, B. K. Panigrahi, P. Subbarao
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引用次数: 7

摘要

现代燃煤电厂需要处理多种煤种,并适应较大的负荷变化。磨煤机将煤磨成所需的细度,一次风干后将粉碎的燃料提供给燃烧器。由于控制简单,磨粉系统内部存在各种故障,磨粉机的动态响应较差。本文提出了一种铣削系统相关重要变量n步超前值的时间序列预测方法。一个简单的,数据驱动的,非参数技术,即k-NN回归用于预测。轧机变量的预测有助于改进控制和优化轧机运行。将该方法应用于提前5分钟预测,并使用印度古吉拉特邦燃煤电厂的实际数据进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of K-NN regression for predicting coal mill related variables
Modern coal fired power plants are required to handle a variety of coal types and accommodate large load changes. Coal mills grind the coal to required fineness and primary air dries and supplies the pulverized fuel to the burners. The dynamic response of coal mills is poor due to simple controls and various faults occurring inside the milling system. In this paper, an approach for time series prediction of n-step ahead values of important variables associated with the milling system is provided. A simple, data driven, non parametric technique i.e. k-NN regression is used for the prediction. The prediction of mill variables is helpful for improving controls and optimizing the mill operation. The proposed approach is applied for 5 minute ahead prediction and validated using the actual data obtained from a coal fired power plant in Gujarat, India.
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