Interval Type-2 Fuzzy Adaptive Strategy for Fault Tolerant Control Based on New Faulty Model Design: Application to DSIM Under Broken Rotor Bars Fault

Interval Type-2 Fuzzy Adaptive Strategy for Fault Tolerant Control Based on New Faulty Model Design: Application to DSIM Under Broken Rotor Bars Fault

Noureddine LayadiSamir Zeghlache Ali Djerioui Hemza Mekki Azeddine Houari Mohamed-Fouad Benkhoris Fouad Berrabah 

Laboratoire de Génie Electrique, Department of Electrical Engineering, Faculty of Technology, University Mohamed Boudiaf of M’Sila, BP 166, Ichbilia 28000, Algeria

IREENA Laboratory, University of Nantes, Saint-Nazaire, France

Department of Electrical Engineering, Faculty of Technology, University Mohamed Boudiaf of M’Sila, BP 166, Ichbilia 28000, Algeria

Corresponding Author Email: 
layadinoureddine1@gmail.com
Page: 
212-221
|
DOI: 
https://doi.org/10.18280/mmc_a.910407
Received: 
12 August 2018
|
Accepted: 
30 November 2018
|
Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

This paper presents a fault tolerant control (FTC) based on the type-2 fuzzy logic system (IT2FLS) using an adaptive control law for a double star induction machine (DSIM) under broken rotor bars (BRB) fault of a squirrel-cage in order to improve its reliability and availability. The adaptive fuzzy control is designed to compensate for the fault effect. The proposed FTC is able to maintain acceptable performance in the event of BRB. The stability of the closed-loop is verified by exploitation of Lyapunov theory. To proof the performance and effectiveness of the proposed FTC, a comparative study within sliding mode control (SMC) is carried out. Obtained results show that the proposed FTC has a better robustness against the BRB fault.

Keywords: 

double star induction machine, interval type-2 fuzzy logic system, adaptive control, sliding mode control, fault tolerant control, broken rotor bars

1. Introduction
2. Dsim Faulty Model
3. Design of an Interval Type-2 Fuzzy Logic Adaptive Controller for DSIM
4. Simulation Results and Comparisons
5. Conclusion
Appendix A
Appendix B
  References

[1] Layadi N, Zeghlache S, Benslimane T, Berrabah F. (2017). Comparative analysis between the rotor flux oriented control and backstepping control of a double star induction machine (DSIM) under open-phase fault. AMSE Journals, Series Advances C 72(4): 292-311.

[2] Rouaibia R, Arbaoui F, Bahi T. (2017). Sliding fault eccentricity diagnosis in variable speed induction motor drive using DWT. AMSE Journals, Series Advances C 72(3): 181-202.

[3] Lizarraga-Morales RA, Rodriguez-Donate C, Cabal-Yepez E, Lopez-Ramirez M, Ledesma-Carrillo LM, Ferrucho-Alvarez ER. (2017). Novel FPGA-based methodology for early broken rotor bar detection and classification through homogeneity estimation. IEEE Transactions on Instrumentation and Measurement 66(7): 1760-1769. https://doi.org/10.1109/TIM.2017.2664520

[4] Elbouchikhi E, Choqueuse V, Auger F, Benbouzid MEH. (2017). Motor current signal analysis based on a matched subspace detector. IEEE Transactions on Instrumentation and Measurement 66(12): 3260-3270. https://doi.org/10.1109/TIM.2017.2749858

[5] Hou Z, Huang J, Liu H, Wang T, Zhao L. (2016). Quantitative broken rotor bar fault detection for closed-loop controlled induction motors. IET Electric Power Applications 10(5): 403-410. https://doi.org/10.1049/iet-epa.2015.0440

[6] Belhamdi S, Goléa A. (2017). Fuzzy sliding mode speed controller design of induction motor drives with broken bars. AMSE Journals, Series Advances C 72(4): 281-291.

[7] Sousa KM, da Costa IBV, Maciel ES, Rocha JE, Martelli C, da Silva JCC. (2017). Broken bar fault detection in induction motor by using optical fiber strain sensors. IEEE Sensors Journal 17(12): 3669-3676. https://doi.org/10.1109/JSEN.2017.2695961

[8] Belhamdi S, Goléa A. (2017). Direct field-oriented control using fuzzy logic Type-2 for induction motor with broken rotor bars. AMSE Journals, Series Advances C 72(4): 203-212.

[9] Listwan J, Pieńkowski K. (2016). Sliding-mode direct field-oriented control of six-phase induction motor. Technical Transactions (2-M): 95-108. https://doi.org/10.5277/PED160106

[10] Betin FF, Capolino GA, Fnaiech F. (2010). Fuzzy logic and sliding-mode controls applied to six-phase induction machine with open phases. IEEE Transactions on Industrial Electronics 57(1): 354-364. https://doi.org/10.1109/TIE.2009.2034285

[11] Bounar N, Boulkroune A, Boudjema F, Farza M. (2015). Adaptive fuzzy vector control for a doubly-fed induction motor. Neurocomputing 151(2): 756-769. https://doi.org/10.1016/j.neucom.2014.10.026

[12] Mekki H, Benzineb O, Boukhetala D, TadjineM, Benbouzid  M. (2015). Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems. ISA Transactions (57): 340-351. https://doi.org/10.1016/j.isatra.2015.02.004

[13] Masumpoor S, Khanesar MA. (2015). Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor. Expert Systems with Applications 42(19): 6635-6647. https://doi.org/10.1016/j.eswa.2015.04.046

[14] González-Prieto I, Duran MJ, Barrero FJ. (2017). Fault-tolerant control of six-phase induction motor drives with variable current injection. IEEE Transactions on Power Electronics 32(10): 7894-7903. https://doi.org/10.1109/TPEL.2016.2639070

[15] Mahmoud EA, Abdel-Khalik AS, Soliman HF. (2016). An improved fault tolerant for a five-phase induction machine under open gate transistor faults. Alexandria Engineering Journal 55(3): 2609-2620. https://doi.org/10.1016/j.aej.2016.04.040

[16] Bednarz S. (2017). Rotor fault compensation and detection in a sensorless induction motor drive. Power Electronics and Drives 2(1): 71-80.

[17] Trujillo-Guajardo LA, Rodriguez-Maldonado J, Moonem MA, Platas-Garza MA. (2018). A multi resolution Taylor-Kalman approach for broken rotor bar detection in cage induction motors. IEEE Transactions on Instrumentation and Measurement 67(6): 1317-1328. https://doi.org/10.1109/TIM.2018.2795895