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Tytuł artykułu

Nonlinear Optimal Control for a Gas Compressor Actuated by a Five-Phase Induction Motor

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article proposes a nonlinear optimal control method for the dynamic model of a gas centrifugal compressor being actuated by a five-phase induction motor (5-phase IM). To achieve high torque and high power in the functioning of gas compressors, 5-phase IM appear to be advantageous in comparison to three-phase synchronous or asynchronous electric machines. The dynamic model of the integrated compression system, which comprises the gas compressor and the 5-phase IM, is first written in a nonlinear and multivariable state-space form. It is proven that the electrically driven gas-compression system is differentially flat. Next, this system is approximately linearised around a temporary operating point that is recomputed at each sampling interval. The linearisation is based on first-order Taylor series expansion and uses the computation of the Jacobian matrices of the state-space model of the integrated system. For the linearised state-space description of the compressor and 5-phase IM, a stabilising optimal (H-infinity) feedback controller is designed. This controller achieves a solution to the nonlinear optimal control problem of the compressor and 5-phase IM system under model uncertainty and external perturbations. The feedback gains of the controller are computed by solving an algebraic Riccati equation at each iteration of the control method. Lyapunov analysis is used to demonstrate global stability for the control loop. Additionally, the H-infinity Kalman filter is used as a robust state estimator, which allows for implementing sensorless control for the gas compression system.
Wydawca
Rocznik
Strony
196--218
Opis fizyczny
Bibliogr. 45 poz., rys., tab.
Twórcy
  • Unit of Industrial Automation, Industrial Systems Institute, Rion Patras 26504, Greece
  • Department of ECS Engineering, Rensselaer Polytechnic Institute, New York 12065, USA
autor
  • Department of Electrical Engineering, University of Setif I, Setif 19000, Algeria
autor
  • Department of Innovation Systems, University of Salerno, Fisciano 84084, Italy
Bibliografia
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  • Arahal, M. R., Martin, C., Kowal, A., Castillo, M. and Barrero, F. (2020a). Cost Function Optimization for Predictive Control of A Five-Phase IM. Optimal Control Applications and Methods, 41(1), pp. 84-93.
  • Arahal, M. R., Martin, C., Barrero, F. and Duran, M. J. (2020b). Assessing Variable Sampling Time Controllers for Five-Phase Induction Motor Drives. IEEE Transactions on Industrial Electronics, 67(4), pp. 2523-2532.
  • Arashloo, R. S., Salehifar, M., Romeral, L. and Sala, V. (2019). A Robust Predictive Current Controller for Healthy and Open Circuit Faulty Condition of Five-Phase BLDC Drives Applicable for Wind Generators and Electric Vehicles. Energy Conversion and Management, 92(1), pp. 437-447.
  • Basseville, M., and Nikiforov, I. (1993). Detection of Abrupt Changes: Theory and Applications, Prentice-Hall.
  • Behegen, B., and Gravdahl, T.M. (2008). Active Surge Control of Compression System Using Drive Torque. Automatica, 44(4), 1135-1140.
  • Bernudes, M., Martin, C., Gonzalez-Prieto, I., Duran, M. J., Arahal, M. R. and Barrero, F. (2020). Predictive Current Control in Electrical Drives: An Illustrative Review with Case Examples Using A Five-Phase Induction Motor with Drive Distributed Winding. IET Electic Power Applications, 14(8), 1291-1310.
  • Budinis, S. and Thornhill, M. F. (2018). Control of centrifugal compressors via model predictive control for enhanced oil recovery applications. In: IFAC 2nd Workshop on Automatic Control of Offshore Oil and Gas Production, Florianapolis, Brazil.
  • Durantay, L., Gelin, A., Thibaut, E. and Vidalenc, Y. (2019a), Integrated moto-compressor versus conventional solution. In: IEEE PCIC 2019, IEEE Petroleum and Chemical Industry Conference, Paris, France, May 2019.
  • Durantay, L., Alban, T., Siala, S. and Billaud, A. (2019b), Selection and Tests of Innovative Variable-Speed Motor-Compressor Solutions for A 55MW Full Electric Offshore Platform Maximizing Availability and Efficiency with Better Environmental Impact. IEEE Transactions on Industry Applications, 55(6), pp. 6678-6689.
  • Echeich, M., Trabelsi, R., Kesraoui, H., Iqbal, A. and Mimouni, M. F. (2020). Torque Ripple Improvement of Direct Torque Controlled Five-Phase Induction Motor Drive Using Backstepping Control. International Journal of Power Electronics and Drive Systems, 11(1), pp. 64-74.
  • Echeikh, H., Trabelsi, R., Iqbal, A. and Mimouni, M. R. (2018). Real-Time Implementation of Indirect Rotor Flux Oriented Control of A Five-Phase Induction Motor with Novel Rotor Resistance Adaptation Using Sliding-Mode Observer. Journal of the Franklin Institute, 335(5), pp. 2112-2141.
  • Echeikh, M., Trabelsi, R., Iqbal, A., Bianchi, N. and Mimouni, M. F.(2016). Comparative Study Between the Rotor Flux Oriented Control and Nonlinear Backstepping Control of A Five-Phase Induction Motor Drive an Experimental Validation. IET Power Electronics, 9(3), pp. 2510-2521.
  • Gonzalez-Prieto, L., Duran, M. J., Rios-Garcia, N., Barrera, F. and Martin, C. (2018). Open-Switch Fault Detection in Five-Phase Induction Motor Drives Using Model-Predictive Control. IEEE Transactions on Industrial Electronics, 65(4), pp. 3045-3055.
  • Gravdahl, J. T., Egeland, O. and Vatland, S. O. (2002). Drive Torque Actuation in Active Surge Control of Centrifugal Compressors. Automatica, 38(11), pp. 1881–1893.
  • Han, X., Liu, C., Chen, B. and Zhang, S. (2022). Surge Disturbance Suppression of AMB-Rotor Systems in Magnetically Suspended Centrifugal Compressors. IEEE Transactions on Control Systems Technology, 30(4), pp. 1550-1560.
  • Khadar, S., Abu-Rub, H. and Kouzou, A. (2021). Sensorless Field-Oriented Control for Open-End Winding Five-Phase Induction Motor with Parameters Estimation. IEEE Open Access Journal of the Industrial Electronics Society, 2, pp. 265-278.
  • Li, T., Ma, R. and Han, W. (2020). Virtual Vector-Based Model Predictive Current Control of Five-Phase PMSM with Stator Current and Concentrated Disturbance Observer. IEEE Access, 8, pp. 212635-212646.
  • Lu, Y., Wang, P., Jin, M. and Qi, Y. (2016). Centrifugal Compressor Fault Diagnosis Based on Qualitative Simulation and Thermal Parameters. Mechanical Systems and Signal Processing, 81, pp. 253–273.
  • Ma, X., Zhang, S. and Wang, K. (2019). Active Surge Control for Magnetically Suspended Centrifugal Compresors Using A Variable Equilibrium Point Approach. IEEE Transactions on Industrial Electronics, 66(12), pp. 9363-9393.
  • Martin, C., Arahal, M. R., Barrera, F. and Duran, M. J. (2016). Five-Phase Induction Motor Rotor Current Observer for Finite Control Set Model Predictive Control of Stator Current. IEEE Transactions on Industrial Electronics, 63(7), pp. 4527-4538.
  • Mochammad, S., Kong, Y. J., Noh, Y., Park, S. and Ahn, B. (2021). Stable hybrid feature selection method for compressor fault diagnosis. IEEE Access, 9: pp. 97415-97429.
  • Morawiec, M. and Wilczynski, F. (2022). Srrong Strategy of A Five-Phase Induction Machine Supplied by the Current Source Inverter with the Third Harmonic Injection. IEEE Transactions on Power Electronics, 37(8), pp. 9539-9550.
  • Morawiec, M., Stranlowkski, P., Lewicki, A., Guzinski, J. and Wilczynski, F. (2020). Feedback Control of Multiphase Induction Machines with Backstepping Techniques. IEEE Transactions on Industrial Electronics, 67(6), pp. 4305-4314.
  • Priestley, M., Fletcher, J. E. and Tan, C. (2018). Space-Vector PWM Technique for Five-Phase Open-End Winding PMSM Drive Operating in the Overmodulation Region. IEEE Transactions on Industrial Electronics, 65(9), pp. 6816-6827.
  • Rigatos. G, (2016). Intelligent Renewable Energy Systems: Modelling and Control. Springer.
  • Rigatos, G. (2011). Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms in Robotics and Industrial Engineering. Springer.
  • Rigatos, G. (2015). Nonlinear Control and Filtering Using Differential Flatness Approaches: Applications to Electromechanical Systems. Springer.
  • Rigatos, G. and Karapanou, E. (2020). Advances in Applied Nonlinear Optimal Control. Cambridge Scholars Publishing.
  • Rigatos, G., and Zhang, Q. (2009). Fuzzy Model Validation Using the Local Statistical Approach. Fuzzy Sets and Systems, 60(7), pp. 882-904.
  • Rigatos, G., Abbaszadeh, M. and Siano, P (2022). Control of Dynamical Nonlinear and Partial Differential Equation Systems: Theory and Applications. IET Publications.
  • Rigatos, G. G. and Tzafestas, S. G. (2007). Extended Kalman Filtering for Fuzzy Modelling and MultiSensor Fusion. Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis, 13(3), pp. 251-266.
  • Riveros, J. A., Barrero, F., Levi, E., Duran, M. J., Toral, S. and Jones, M. (2018). Variable-Speed FivePhase Induction Motor Drive Based on Predictive Torque Control. IEEE Transactions on Industrial Electronics, 60(8), pp. 2957-2968.
  • Saad, K., Abdellah, K., Ahmed, H. and Iqbal, A. (2019). Investigation of SVM-Backstepping Sensorless Control of Five-Phase Open-End Winding Induction Motor Based on Model Reference Adaptive System and Parameter Estimation. Engineering Science and Technology, 22(4), pp. 1013-1026.
  • Singhal, S. (2014). Electric Drive Compressor Systems: High-Speed Turbo Compressors Used in the Oil and Gas Industry. IEEE Industry Applications Magazine, 20(6), pp. 52–63.
  • Soleymani, R., Nekani, M. A. and Mourefianpour, M. (2019). A Novel Robust Fault Detection Method for Induction Motor Rotor by Using Unknown Input Observer. Systems Science and Control Engineering, 7(1), pp. 109-115.
  • Tessarolo, A., Ziocco, G. and Tonello, C. (2011), Design and Testing of a 45MW 100Hz Quadruple-Star Synchronous Motor for A Liquified Natural Gas Turbo Compressor Drive. IEEE Transactions on Industry Applications, 47(3), pp. 1210-1219.
  • Torrisi, G., Jaramillo, V., Ottewill, J. R., Mariethoz, M., Morari, M. and Smith, R. S. (2015). Active Surge Control of Electrically Driven Cntrifugal Compressors. In: 2015 European Control Conference, Linz, Austria.
  • Torrissi, G., Grammatico, S., Cortinovis, M., Mercangoz, M., Morari, M. and Smith, R. S. (2019). Model Predictive Approaches for Active Surge Control in Centrifugal Compressors. IEEE Transactions on Control Systems Technology, 29(6), pp. 1947–1960.
  • Toussaint, G. J., Basar, T. and Bullo, F. (2000), H∞ Optimal Tracking Control Techniques for Nonlinear Underactuated Systems. In: Proceedings of the 39th IEEE Conference on Decision and Control (IEEE CDC 2000), Sydney Australia, 12-15 December 2000.
  • Tribelsi, M. and Semail, E. (2021). Virtual Current Vector-Based Method for Inverter Open-Switch and Openphase Fault Diagnosis in Multi-Phase Permanent Magnet Synchronous Motor Drives. IET Electric Power Applications, 16(2), pp. 1476-1491.
  • Verma, M., Parker, D., Grindbaum, F. and Nanney, J. (2017). Making the Leap to Electric Motors and Adjustable Speed Drives: Case-Study of the 20000Hp Gas-Turbine-Driven Compressor. IEEE Industry Applications Magazine, 23(6), pp. 29–38
  • Xiang, S. and Li, J. (2022). Cascade Model Predictive Current Control for Five-Phase Permanent Magnet Synchronous Motor. IEEE Access, 10, pp. 88812-88820.
  • Xiong, C., Xu, H., Guan, T. and Zhou, P. (2020). A Constraint Switching Frequency Multiple-Vector-Based Model Predictive Current Control of FivePhase PMSM with Nonsinusoidal Back EMF. IEEE Transactions on Industrial Electronics, 67(3), pp. 695-1707.
  • Zhou, Y., Yan, Z., Duan, Q., Wang, L. and Wu, X. (2019). Direct Torque Control Strategy of Five-Phase PMSM with Load Capacity Enhancement. IET Power Electronics, 12(3), pp. 598-606.
Uwagi
Special Section - Advanced Control Methods of Electrical Machines and Drives.
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3f349e74-1513-4b52-85f3-bce4def91b43
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