A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

Abdelhafid Benyounes, Abdelhamid Iratni, Ahmed Hafaifa, Ilhami Colak

Abstract


This paper focuses on the identification and modeling of gas turbine dynamics, specifically those used in power generation plants. The approach utilizes experimental data and employs fuzzy reasoning systems. The resulting model serves the purpose of approximating nonlinear gas turbine systems and ensuring reliable system control. By incorporating uncertainties associated with human reasoning, such as fuzzy systems based on Takagi-Sugeno reasoning, it is possible to achieve highly reliable control systems. The primary objective of this work is to develop an effective monitoring system by employing nonlinear identification techniques, namely fuzzy systems and neuro-fuzzy systems, based on real-time on-site experimental data. Additionally, the proposed identification approaches are evaluated through a comparative study, where the results obtained using the nonlinear autoregressive exogenous neural network (NARAX-NN Modeling) technique are compared with those obtained using the ANFIS approach. The obtained results further facilitate the comprehension and analysis of the nonlinearities present in these complex systems, ultimately aiding in the prediction of their dynamic behavior.

Keywords


Adaptive system; fuzzy modeling; gas turbine; inference system (ANFIS); neuro-fuzzy system

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References


Abdelhafid Benyounes, Ahmed Hafaifa and Mouloud Guemana, Gas turbine modelling based on fuzzy clustering algorithm using experimental data. Journal of Applied Artificial Intelligence, 2016, vol. 30, no. 1, pp. 29-51.

Abdollah Mehrpanahi, Gholamhasan Payganeh, Mohammadreza Arbabtafti, Dynamic modeling of an industrial gas turbine in loading and unloading conditions using a gray box method. Energy, 2017, vol. 120, pp. 1012-1024.

Achour El hamdaouy, Issam Salhi, Abdellatif Belattar, Said Doubabi, Takagi–Sugeno fuzzy modeling for three-phase micro hydropower plant prototype. International Journal of Hydrogen Energy, 2017, vol. 42, no. 28, pp. 17782-17792.

Ahmed Hafaifa, Mouloud Guemana and Attia Daoudi, Vibration supervision in gas turbine based on parity space approach to increasing efficiency. Journal of Vibration and Control, June 2015, vol. 21, pp.1622-1632.

Ahmed Zohair Djeddi, Ahmed Hafaifa, Nadji Hadroug, Abdelhamid Iratni, Gas turbine availability improvement based on long short-term memory networks using deep learning of their failures data analysis. Process Safety and Environmental Protection, 2022, vol. 159, pp. 1-25.

Babuska R. and Veen P.J., Improved covariance estimation for Gustafson-Kessel clustering. Procedding of IEEE Conference On Fuzzy Systems, 2002, vol. 2, pp. 1081-1085.

Babuška R. and Verbruggen H.B. Identi?cation of composite linear models via fuzzy clustering. Proceeding of the European Control Conference 1995, Rome, Italy, pp. 1207-1212.

Babuska Robert and Verbruggen H.B., A new identification method for linguistic fuzzy models. Proceedings of IEEE International Conference on Fuzzy Systems, 20-24 March 1995, Yokohama, Japan, vol. 2, pp. 905-912.

Balazs Feil, Janos Abonyi, Ferenc Szeifert, Model order selection of nonlinear input–output models––a clustering based approach. Journal of Process Control, 2004, vol. 14, no. 6, pp. 593-602.

Bank Tavakoli M.R, Vahidi B and Gawlik W., An educational guide to extract the parameters of heavy-duty gas turbines model in dynamic studies based on operational data. IEEE Transaction on Power Systems 2009, vol. 24, no. 3, pp. 1366-1374.

Ben Rahmoune Mohamed, Ahmed Hafaifa, Abdellah Kouzou, XiaoQi Chen, Ahmed Chaibet, Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling. Mathematics and Computers in Simulation, 2021, vol. 179, pp. 23-47.

Ben Rahmoune Mohamed, Ahmed Hafaifa and Guemana Mouloud, Vibration modeling improves pipeline performance, costs. Oil & Gas Journal, 2015, vol. 113, no. 3, pp. 98-100.

Ben Rahmoune Mohamed, Mouloud Guemana, Ahmed Hafaifa, Reliability modelling using Weibull distribution on real-time system in oil drilling installations. Algerian Journal of Signals and Systems, 2017, vol. 2, no. 4, pp. 189-198.

Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 1984, vol. 10, no. 2-3, pp. 191-203.

Boon Chiang Ng, Intan Zaurah Mat Darus, Hishamuddin Jamaluddin, Haslinda Mohamed Kamar, Dynamic modelling of an automotive variable speed air conditioning system using nonlinear autoregressive exogenous neural networks. Applied Thermal Engineering, 2014, vol. 73, no. 1, pp. 1255-1269.

Choayb Djeddi, Ahmed Hafaifa, Abdelhamid Iratni, Nadji Hadroug, XiaoQi Chen, Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach. Journal of Manufacturing Systems, 2021, vol. 59, pp.190-213.

Daouren F. Akhmetov, Yasuhiko Dote, Shaikh M. Shafique, System identification by the general parameter neural networks with fuzzy self organization. IFAC Proceedings, 1997, vol. 30, no. 11, pp. 795-800.

Do Won Kang, Tong Seop Kim, Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation. Applied Energy, 2018, vol. 212, pp. 1345-1359.

Gustafson DE, Kessel WC. Fuzzy clustering with a fuzzy covariance matrix. In Decision and Control including the 17th Symposium on Adaptive Processes, 1979, 1978 IEEE Conference, pp. 761-766.

Hakim Bagua, Ahmed Hafaifa, Abdelhamid Iratni & Mouloud Guemana, Model variables identification of a gas turbine using a subspace approach based on input/output data measurements. Control Theory and Technology, 2020, vol. 19, pp.183-196.

Houman Hanachi, Jie Liu, Christopher Mechefske, Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system. Chinese Journal of Aeronautics, 2018, vol. 31, no. 1, pp. 1-9.

Hua Xiao, Agustin Valera-Medina, Richard Marsh, Philip J. Bowen, Numerical study assessing various ammonia/methane reaction models for use under gas turbine conditions. Fuel, 2017, vol. 196, pp. 344-351.

Jang J.-S.R., ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 1993, vol. 23, no. 3, pp. 665-685.

Jiandong Duan, Li Sun, Guanglin Wang, Fengjiang Wu, Nonlinear modeling of regenerative cycle micro gas turbine. Energy, 2015, vol. 91, pp. 168-175.

Lazzaretto A. and Toffolo A., Analytical and neural network models for gas turbine design and off design simulation. International Journal of Applied Thermodynamics, 2001, vol. 4, no. 4, pp. 173-182.

Lazzaretto A. and Toffolo A., Prediction of performance and emissions of a two shaft gas turbine from experimental data. Applied Thermal Engineering, 2008, vol. 28, no. 17-18, pp. 2405-2415.

Manjeevan Seera, Kuldeep Randhawa, Chee Peng Lim, Improving the Fuzzy Min–Max neural network performance with an ensemble of clustering trees. Neurocomputing, 2018, vol. 275, pp. 1744-1751.

Merouane Alaoui, Abdelhamid Iratni, Obaid S. Alshammari, Ahmed Hafaifa, Ilhami Colak and Mouloud Guemana, Stability and Analysis of Vibrations Bifurcation Based on Dynamic Modeling of a Solar Titan 130 Gas Turbine. Journal of Mechanical Engineering, 2022, Volume 72, Issue 2, pp. 1-14.

Nadji Hadroug, Ahmed Hafaifa, Bachir Alili, Abdelhamid Iratni, XiaoQi Chen, Fuzzy diagnostic strategy implementation for gas turbine vibrations faults detection: Towards a characterization of symptom–fault correlations. Journal of Vibration Engineering & Technologies, 2022, vol. 10, pp. 225-251.

Nadji Hadroug, Ahmed Hafaifa, Kouzou Abdellah and Ahmed Chaibet, Dynamic model linearization of two shafts gas turbine via their input / output data around the equilibrium points. Energy, 2017, vol. 120, pp. 488-497.

Paolo Giordani, Ana Belén Ramos-Guajardo, A fuzzy clustering procedure for random fuzzy sets. Fuzzy Sets and Systems, 2016, vol. 305, pp. 54-69.

Pierpaolo D'Urso, Elizabeth A. Maharaj, Alonso Andrés M., Fuzzy clustering of time series using extremes. Fuzzy Sets and Systems, 2017, vol. 318, pp. 56-79.

Rowen W.I., Simplified mathematical representations of single-shaft gas turbines in mechanical derive service. Turbomachine International, 1992, vol. 33, no. 5, pp. 26-32.

Sidali Aissat, Ahmed Hafaifa, Abdelhamid Iratni, Mouloud Guemana, XiaoQi Chen, Exploitation of multi-models identification with decoupled states in twin shaft gas turbine variables for its diagnosis based on parity space approach. International Journal of Dynamics and Control, 2022, vol. 10, pp. 25-48.

Takagi T. and Sugeno M., Fuzzy identification of systems and its applications to modeling and control, IEEE transactions on systems, man, and cybernetics, 1985, vol. 15, no. 1, pp. 116-132.

Wiese A.P., Blom M.J., Manzie C., Brear M.J., Kitchener A., Model reduction and MIMO model predictive control of gas turbine systems. Control Engineering Practice, 2015, vol. 45, pp. 194-206.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v7i2.288.g275

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