A feasibility study on using neural networks in performance analysis of coal-fired power plants

Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the Pub Date : 2004-09-27 DOI:10.1109/SSST.2004.1295717

V.V. Kantubhukta, M. Abdelrahman

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引用次数: 3

Abstract

Coal-fired power plants are highly complex nonlinear systems. Several performance-monitoring techniques based on linearization and empirical estimations have been developed. However, there is a need for nonlinear modeling for the power plant performance analysis in order to meet the growing demands of economic and operational requirements. In the present research neural networks are used to model the thermodynamic process of a coal-fired power plant, based on actual plant data and simulated data obtained from mathematical models that provide information that is currently not directly available. A sensitivity analysis study is performed to determine the effect of various plant variables on an essential performance parameter, namely, coal flow rate. The safe operation of a coal-fired power plant also requires correct operation of plant instrumentation. Failed instruments provide inaccurate information on the state of a process, which can lead to undesirable or inefficient operation of the power plant. Artificial neural networks are used to develop the analytical redundancy to infer the state of important plant parameters. A sensitivity analysis study is performed to determine the critical parameters influencing the estimated plant parameters.

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神经网络在燃煤电厂性能分析中的可行性研究

燃煤电厂是高度复杂的非线性系统。已经开发了几种基于线性化和经验估计的性能监测技术。然而，为了满足日益增长的经济和运行要求，电厂的性能分析需要非线性建模。在目前的研究中，神经网络被用于模拟燃煤电厂的热力学过程，基于电厂的实际数据和从数学模型中获得的模拟数据，这些数据提供了目前无法直接获得的信息。进行敏感性分析研究，以确定各种工厂变量对一个基本性能参数，即煤流量的影响。燃煤电厂的安全运行还要求电厂仪表的正确操作。故障仪表提供的过程状态信息不准确，这可能导致电厂的不良或低效运行。利用人工神经网络发展分析冗余来推断装置重要参数的状态。进行了敏感性分析研究，以确定影响估计工厂参数的关键参数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the

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