Model-Based Performance Monitoring with Dynamic Compensation for Heat Utilization Process in Distributed Energy System

A model-based performance monitoring method for heat utilization processes in distributed energy systems is developed in this study. It is characterized by introducing dynamic compensation, where the response lags of heat exchangers to variations in their operating conditions are identified as first-order lag elements, and the output process variables estimated using a static input-output model are revised on the basis of these identified response lags. The estimated values of the output process variables are compared with their measured values in order to detect device failures. A numerical simulation of a heat utilization process in a gas engine cogeneration system containing a radiator with a considerable response lag reveals that the developed performance monitoring method has sufficient estimation accuracy in terms of the output process variables and ability to detect device failures, including a deterioration in the heat transfer performance of the radiator and heat exchanger, in a dynamic state.

[1]  Yong-Jin Joo,et al.  Implementation of on-line performance monitoring system at Seoincheon and Sinincheon combined cycle power plant , 2005 .

[2]  Byung-Cheon Ahn,et al.  Transient pattern analysis for fault detection and diagnosis of HVAC systems , 2005 .

[3]  Ryohei Yokoyama,et al.  On-line model-based performance monitoring of a shell-and-tube type heat exchanger using steam and water , 2008 .

[4]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[5]  Shuichi Umezawa.  Performance Diagnosis using Optical Torque Sensor for Selection of a Steam Supply Plant among Advanced Combined Cycle Power Plants , 2008 .

[6]  Ryohei Yokoyama,et al.  Optimal Operational Planning of a Gas Engine Cogeneration System with Photovoltaic Array (Analysis on Operational Strategy and Energy Saving Effect) , 2012 .

[7]  Zhiwei Lian,et al.  Data mining based sensor fault diagnosis and validation for building air conditioning system , 2006 .

[8]  Tadahiro Machiyama,et al.  Dynamic characteristics of two shell-and-tube heat exchangers connected in parallel , 1995 .

[9]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[10]  Masayuki Tamura.  Designing Empirical Diagnostic Rules for Plant Start-Up Monitoring Using Dynamic Time Warping , 2010 .

[11]  Fu Xiao,et al.  Detection and diagnosis of AHU sensor faults using principal component analysis method , 2004 .

[12]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[13]  Ryohei Yokoyama,et al.  1213 Operational Management of Energy Supply Systems by Integrated Use of Optimization Methods : Hierarchical Integration Approach , 2008 .

[14]  Toru Takahashi,et al.  Development of Performance Deterioration Diagnosis Method for Gas Turbine Combined Cycle Power Plants , 2010 .

[15]  Ryohei Yokoyama,et al.  Model-based Performance Monitoring of Heat Exchange Process in Transient State , 2009 .

[16]  Enrico Sciubba,et al.  Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems , 2004 .