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Fault detection for DFIG based on sliding mode observer of new reaching law

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
For fault detection of doubly-fed induction generator (DFIG), in this paper, a method of sliding mode observer (SMO) based on a new reaching law (NRL) is proposed. The SMO based on the NRL (NRL- SMO) theoretically eliminates system chatter caused by the reaching law and can be switched in time with system interference in terms of robustness and smoothness. In addition, the sliding mode control law is used as the index of fault detection. Firstly, this paper gives the NRL with the theoretically analyzes. Secondly, according to the mathematical model of DFIG, NRL-SMO is designed, and its analysis of stability and robustness are carried out. Then this paper describes how to choose the optimal parameters of the NRL-SMO. Finally, three common wind turbine system faults are given, which are DFIG inter-turn stator fault, grid voltage drop fault, and rotor current sensor fault. The simulation models of the DFIG under different faults is established. The simulation results prove that the superiority of the method of NRL-SMO in state tracking and the feasibility of fault detection.
Rocznik
Strony
art. no. e137389
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
autor
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
autor
  • School of Physics and Electronics, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
autor
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
autor
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
autor
  • School of Information and Electrical Engineering, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
  • Key Laboratory of Knowledge Processing Networked Manufacturing, Hunan University of Science and Technology, Hunan Pro., Xiangtan,411201, China
Bibliografia
  • [1] Z. Hameed, Y.S. Hong, Y.M. Cho, S.H. Ahn, and C.K. Song. “Condition monitoring and fault detection of wind turbines and related algorithms: A review”, Renew. Sust. Energ. Rev. 13(1), 1‒39 (2009).
  • [2] A. Stefani, A. Yazidi, C. Rossi, F. Filippetti, D. Casadei, and G.A. Capolino, “Doubly fed induction machines diagnosis based on signature analysis of rotor modulating signals”, IEEE Trans. Ind. Appl. 44(6), 1711‒1721(2008).
  • [3] D. Shah, S. Nandi, and P. Neti, “Stator-interturn-fault detection of doubly fed induction generators using rotor-current and search-coil-voltage signature analysis”, IEEE Trans. Ind. Appl. 45(5), 1831‒1842 (2009).
  • [4] G. Stojčić, K. Pašanbegović, and T.M. Wolbank, “Detecting faults in doubly fed induction generator by rotor side transient current measurement”, IEEE Trans. Ind. App. 50(5), 3494‒3502 (2014).
  • [5] R. Roshanfekr and A. Jalilian, “Wavelet-based index to discriminate between minor inter-turn short-circuit and resistive asymmetrical faults in stator windings of doubly fed induction generators, a simulation study”, IET Gener. Transm. Distrib. 10(2), 374‒381 (2016).
  • [6] M.B. Abadi et al., “Detection of stator and rotor faults in a DFIG based on the stator reactive power analysis”, in IECON 2014‒40th Annual Conference of the IEEE Industrial Electronics Society 2014 pp. 2037‒2043.
  • [7] S. He, X. Shen, and Z. Jiang, “Detection and Location of Stator Winding Interturn Fault at Different Slots of DFIG”, IEEE Access 7, 89342‒89353 (2019).
  • [8] I. Erlich, C. Feltes, and F. Shewarega, “Enhanced voltage drop control by VSC–HVDC systems for improving wind farm fault ridethrough capability”, IEEE Trans. Power Deliv. 29(1), 378‒385 (2013).
  • [9] Ö. Göksu, R. Teodorescu, C.L. Bak, F. Iov, and P.C. Kjær, “Instability of wind turbine converters during current injection to low voltage grid faults and PLL frequency based stability solution”, IEEE Trans. Power Syst. 29(4), 1683‒1691 (2014).
  • [10] Z. Fan, G. Song, X. Kang, J. Tang, and X. Wang, “Three-phase fault direction identification method for outgoing transmission line of DFIG-based wind farms”, J. Mod. Power Syst. 7(5), 1155‒1164 (2019).
  • [11] L.G. Meegahapola, T. Littler, and D. Flynn, “Decoupled-DFIG fault ride-through strategy for enhanced stability performance during grid faults”, IEEE Trans. Sustain. Energy 1(3), 152‒162 (2010).
  • [12] F. Aguilera, P.M. De la Barrera, C.H. De Angelo, and D.E. Trejo, “Current-sensor fault detection and isolation for induction-motor drives using a geometric approach”, Control Eng. Pract. 53, 35‒46 (2016).
  • [13] S. Abdelmalek, S. Rezazi, and A.T. Azar, “Sensor faults detection and estimation for a DFIG equipped wind turbine”, Energy Procedia 139, 3‒9 (2017).
  • [14] M. Liu and P. Shi, “Sensor fault estimation and tolerant control for Itô stochastic systems with a descriptor sliding mode approach”, Automatica 49(5), 1242‒1250 (2013).
  • [15] Y.J. Kim, N. Jeon, and H. Lee, “Model based fault detection and isolation for driving motors of a ground vehicle”, Sens. Transducers 199(4), 67 (2016).
  • [16] K. Xiahou, Y. Liu, L. Wang, M.S. Li, and Q.H. Wu, “Switching fault-tolerant control for DFIG-based wind turbines with rotor and stator current sensor faults”, IEEE Access 7, 103390‒103403 (2019).
  • [17] K.S. Xiahou, Y. Liu, M.S. Li, and Q.H. Wu, “Sensor fault-tolerant control of DFIG based wind energy conversion systems”, Int. J. Electr. Power Energy Syst. 117, 105563 (2020).
  • [18] Z.Y. Xue, K.S. Xiahou, M.S. Li, T.Y. Ji, and Q.H. Wu, “Diagnosis of multiple open-circuit switch faults based on long short-term memory network for DFIG-based wind turbine systems”, IEEE J. Emerg. Sel. Top. Power Electron. 8(3), 2600‒2610 (2019).
  • [19] L. Jing, M. Zhao, P. Li, and X. Xu, “A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox”, Measurement 111, 1‒10 (2017).
  • [20] W. Teng, H. Cheng, X. Ding, Y. Liu, Z. Ma, and H. Mu, “DNNbased approach for fault detection in a direct drive wind turbine”, IET Renew. Power Gener. 12(10), 1164‒1171 (2018).
  • [21] M.N. Akram and S. Lotfifard, “Modeling and health monitoring of DC side of photovoltaic array”, IEEE Trans. Sustain. Energy 6(4), 1245‒1253 (2015).
  • [22] W. Gao and J.C. Hung, “Variable structure control of nonlinear systems, A new approach”, IEEE Trans. Ind. Electron. 40(1), 45‒55 (1993).
  • [23] C.J. Fallaha, M. Saad, H.Y. Kanaan, and K. Al-Haddad, “Sliding-mode robot control with exponential reaching law”, IEEE Trans. Ind. Electron. 58(2), 600‒610 (2010).
  • [24] Y. Liu, Z. Wang, L. Xiong, J. Wang, X. Jiang, G. Bai, R. Li, S. Liu, “DFIG wind turbine sliding mode control with exponential reaching law under variable wind speed”, Int. J. Electr. Power Energy Syst. 96, 253‒260 (2018).
  • [25] Z. Lan, L. Li, C. Deng, Y. Zhang, W. Yu, and P. Wong, “A novel stator current observer for fault tolerant control of stator current sensor in DFIG”, in 2018 IEEE Energy Conversion Congress and Exposition (ECCE), 2018, pp. 790‒797.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7e1f8eb4-ce76-4ffd-be32-1d2b876e6eda
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