Fault diagnosis of sensors in the control system of a steam turbine

• Autorzy: Pawlak Mariusz , Buchta Janusz , Oziemski Andrzej

Warianty tytułu

PL

Diagnostyka uszkodzeń torów pomiarowych w układzie sterowania turbiny parowej

• Języki publikacji: EN

Abstrakty

EN

A diagnostic and control system for a turbine is presented. The influence of the turbine controller on regulation processes in the power system is described. Measured quantities have been characterized and methods for detecting errors have been determined. The paper presents the application of fuzzy neural networks (fuzzy-NNs) for diagnosing sensor faults in the control systems of a steam turbine. The structure of the fuzzy-NN model and the model’s method of learning, based on measurement data, are presented. Fuzzy-NNs are used to detect faults procedures. The fuzzy-NN models are created and verified.

PL

Przedstawiono system diagnostyki dla układu sterowania turbiny parowej. Opisano procesy regulacji w systemie elektroenergetycznym oraz strukturę układu regulacji turbiny kondensacyjnej w układzie bloku energetycznego. Mierzone wielkości zostały scharakteryzowane wraz z metodami wykrywania uszkodzeń dla poszczególnych wielkości. W pracy przedstawiono zastosowanie rozmytych sieci neuronowych do detekcji uszkodzeń torów pomiarowych Przedstawiono strukturę modelu rozmytego i metodę uczenia modelu na podstawie danych pomiarowych. Zaprezentowano przykład zastosowania modelu FNN i zweryfikowano jego działanie na podstawie rzeczywistych danych pomiarowych.

Słowa kluczowe

EN

diagnostic; turbine; control system; neural networks

PL

diagnostyka; turbina; system sterowania; sieci neuronowe

• Wydawca: Uniwersytet Technologiczno-Humanistyczny im. Kazimierza Pułaskiego w Radomiu
• Czasopismo: Journal of Automation, Electronics and Electrical Engineering
• Rocznik: 2019
• Tom: Vol. 1, No. 1
• Strony: 29--36
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Pawlak Mariusz

• Lodz University of Technology , Faculty of Electrical, Electronic, Computer and Control Engineering, Bohdana Stefanowskiego 18/22, 90-924 Łódź, Poland

autor

Buchta Janusz

• Lodz University of Technology , Faculty of Electrical, Electronic, Computer and Control Engineering, Bohdana Stefanowskiego 18/22, 90-924 Łódź, Poland

autor

Oziemski Andrzej

• Lodz University of Technology , Faculty of Electrical, Electronic, Computer and Control Engineering, Bohdana Stefanowskiego 18/22, 90-924 Łódź, Poland

Bibliografia

• [1] Guerrero M.J., Peng T., Gui W. (2016) “Open-Switch Fault Diagnosis and Fault Tolerant for Matrix Converter With Finite Control Set-Model Predictive Control”, IEEE Transactions on Industrial Electronics, Vol. 63, No.9.
• [2] Schuh M., Zgorzelski M., Lunze J. (2015) “Experimental evaluation of an active fault–tolerant control method”. Control Engineering Practice, vol. 43, pp. 1–11.
• [3] Paoli A., Sartini M., Lafortuneb S. (2011) “Active Fault Tolerant Control of Discrete Event Systems Using Online Diagnostics” Automatica, vol 47, pp. 639-649.
• [4] Calado J.M.F. et al. (2001), “Soft computing approaches to fault diagnosis for dynamic systems” European Journal of Control; vol. 7 (2–3): pp. 248–286.
• [5] Chiang L.H., Russell E.L., Braatz R.D. (2001) “Fault Detection and Diagnosis in Industrial Systems”. London, Springer.
• [6] Blanke M., Kinnaert M., Lunze L., Staroswiecki M. (2004) “Diagnosis and Fault-Tolerant Control”, Berlin, Springer-Verlag.
• [7] Zhang J., Morris A.J., Martin E.B. (1996) “Robust process fault detection and diagnosis using neuro-fuzzy networks” presented at the 13th Triennial World Congress, San Francisco, USA, 7f-05 pp. 169-174.
• [8] Candau J., de Miguel L.J., Ruiz J.G. (1997) “Controller reconfiguration system using parity equations and fuzzy logic” IFAC Symposium on Fault Detection, Supervision and Safety for Technical Process – SAFEPROCESS’97 , Hull, UK, Vol.2, 1258-1263.
• [9] Yang F., Zhang H., Hui G., Wang S. (2012) “Mode-independent fuzzy fault-tolerant variable sampling stabilization of nonlinear networked systems with both time-varying and random delays”, Fuzzy Sets Systems, vol. 207, pp. 45–63.
• [10] Li P., Yang G. (2011) “An adaptive fuzzy design for fault-tolerant control of MIMO nonlinear uncertain systems” Journal Control Theory Appl., vol. 9, no. 2, 244–250.
• [11] Blesa J., Rotondo D., Puig V., Nejjari F. (2014), “FDI and FTC of wind turbines using the interval observer approach and virtual actuators/sensors”, Control Engineering Practice vol. 24, pp. 138-155.
• [12] Liu X.J, Kong X.B., Hou G.L ., Wang J. (2013), ”Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzyneural network methods”, Energy Conversion and Management, vol. 65, pp. 518–527.
• [13] Jonghoon A., Soolyeon Ch. (2017) “Dae Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands”, Applied Energy, vol.190, pp.222–231.
• [14] Liu X.J., Lara-Rosanoa F., Chan C.W. (2003) ”Neuro-fuzzy network modelling and control of steam pressure in 300MW steam-boiler system”, Engineering Applications Artificial Intelligence, vol. 16:431–40.
• [15] Petković D., Cojbaśićc Z., Nikolić V., Shamshirband S., Kiah L.M., Anuar N.B., Wahid A., Wahab A. (2014) “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission”, Energy, vol. 64, pp. 868-874.
• [16] Hafaifa A., Kouzoul A., Benyounes M.G. (2016) “Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification Mathematics-in-Industry Case Studies”, vol.7:4, 2016.
• [17] Pawlak M. (2016) “Water Level Control System for a Boiler Drum of a Power Boiler Resistant to Measuring Track Damage”, Maintenance Problems, vol. 2/2016 (101), pp.135-144.
• [18] Pawlak M. (2018) “Active Fault Tolerant Control System for the Measurement Circuit in a Drum Boiler Feed-Water Control System”, Measurement and Control, vol. 51, Issue 1-2, pp. 4-15 .

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).