Estimating Compressor Discharge Pressure of Gas Turbine Power Plant Using Type-2 Fuzzy Logic Systems

This paper presents a successful demonstration of application of type-2 fuzzy logic systems (FLS) to predict a critical parameter of Gas Turbine in a power plant viz., the compressor discharge pressure. The loss of Compressor Discharge Pressure (CDP) measurement leads to loss of few megawatts electricity generation. It is also demonstrated here by way of comparison, that a type-2 FLS is more robust in the presence of noise uncertainties than a type-1 conventional FLS for this application. Results are verified through the practical plant data obtained from a 110 MW gas turbine power plant.

[1]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[2]  K. Mathioudakis,et al.  Setting Up of a Probabilistic Neural Network for Sensor Fault Detection Including Operation With Component Faults , 2002 .

[3]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[4]  J. Wilkie,et al.  An integrated robust/neural controller with gas turbine applications , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[5]  Ranjan Ganguli,et al.  Application of Fuzzy Logic for Fault Isolation of Jet Engines , 2001 .

[6]  J. Mendel Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[7]  Allan J. Volponi,et al.  Fuzzy Fuel Flow Selection Logic for a Real Time Embedded Full Authority Digital Engine Control , 2003 .

[8]  Henry Cohen,et al.  Gas turbine theory , 1973 .

[9]  K. K. Botros,et al.  A demonstration of artificial neural-networks-based data mining for gas-turbine-driven compressor stations , 2002 .

[10]  Allan J. Volponi,et al.  The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study , 2000 .

[11]  Jerry M. Mendel,et al.  Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[12]  Vasile Palade,et al.  FAULT DIAGNOSIS OF AN INDUSTRIAL GAS TURBINE USING NEURO-FUZZY METHODS , 2002 .

[13]  Hans R. Depold,et al.  The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics , 1998 .

[14]  Giovanni Cerri,et al.  A Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section , 2000 .

[15]  Ravi Rajamani,et al.  Estimating Gas Turbine Internal Cycle Parameters Using a Neural Network , 1996 .

[16]  K. K. Botros,et al.  A Demonstration of Artificial Neural Networks Based Data Mining for Gas Turbine Driven Compressor Stations , 2000 .

[17]  Jerry M. Mendel,et al.  Uncertainty versus choice in rule-based fuzzy logic systems , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).