Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In the paper, an analysis is made of the stator current sensor fault detector based on artificial neural network for vector controlled induction motor drive system. The systems with different learning algorithms and structures are analyzed and tested in different drive conditions. Simulation results are obtained in direct torque control algorithm (DTC-SVM) and performed in MATLAB/SimPowerSystem software.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Strony
127--138
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
- Wrocław University of Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
- [1]ALAG S., GOEBEL K., AGOGINO A., A Methodology for Intelligent Sensor Validation and Fusion Used in Tracking and Avoidance of Objects for Automated Vehicles, Proc. IEEE Am. Control Conf. 1995, 3647–3653.
- [2]ADOUNI A., BEN HAMED M., FLAH A., SBITA L., Sensor and actuator fault detection and isolation based on artificial neural networks and fuzzy logic applied on induction motor, 2013 Int. Conf. on Control, Decision and Information Technologies CoDIT, 2013.
- [3]RUSIECKI A., Neural networks learning algorithms robust to data errors, Ph.D. dissertation, Wrocław University of Technology, Poland, 2007.
- [4]BLANKE M., KINNAERT M., LUNZE J., Diagnosis and fault-tolerant control, Springer-Verlag, 2003.
- [5]DYBKOWSKI M., Estimation of speed in a vector controlled induction motor drive – selected problems, Scientific Papers of the Institute of Electrical Machines, Drives and Measurements of Wrocław Univ. of Technology. No. 67, ser. Monographs, No. 20, Wrocław, Poland, 2013, (in Polish).
- [6]ORŁOWSKA-KOWALSKA T., Sensorless induction motor drives, Oficyna Wydawnicza Politechniki Wrocławskiej, Wroclaw, Poland, 2003, (in Polish).
- [7]ISERMANN R., Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance, Springer, New York 2006.
- [8]KOWALSKI C.T., Monitoring and diagnostics of the induction motor faults using neural networks, Scientific Papers of the Institute of Electrical Machines, Drives and Measurements of Wrocław Univ. of Technology, No. 57. ser. Monographs No. 20, Wrocław, Poland, 2005.
- [9]GAEID K., Fault Tolerant Control of Induction Motor, Modern Applied Science, 2011, Vol. 5, No. 4.
- [10]JIANG L., Sensor fault detection and isolation using system dynamics identification techniques, Ph.D. dissertation, The University of Michigan, 2011.
- [11]KLIMKOWSKI K., Compensation algorithm based on hardware redundancy of chosen measurement sensors faults in drive systems with induction motor, The XII National Conference Control in Power Electronics and Electric Drives, SENE’2015, Łódź, Poland, 2015, 1–8, (in Polish).
- [12]VAS P., Artificial-intelligence-based electrical machines and drives, Oxford University Press, Oxford 1999.
- [13]OSOWSKI S., Neural Networks for Information Processing, Warsaw University of Technology Press, Warsaw, 2006.
- [14]LIVIERIS I.E., PINTELAS P., Performance evaluation of descent CG methods for neural network training, 9th Hellenic European Research on Computer Mathematics its Applications Conference HERCMA’09, 2009, 40–46.
- [15]LIKAS A., STAFYLOPATIS A., Training the random neural network using quasi-Newton methods, Eur. J. Operat. Res., 2000, Vol. 126, 331–339.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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