Advertisement

High-gain observer-based sensorless control of a flywheel energy storage system for integration with a grid-connected variable-speed wind generator

  • M. Mansour
  • S. Hadj Saïd
  • S. BendoukhaEmail author
  • W. Berrayana
  • M. N. Mansouri
  • M. F. Mimouni
Methodologies and Application
  • 12 Downloads

Abstract

This paper introduces an induction machine-based flywheel energy storage system (FESS) for direct integration with a variable-speed wind generator (VSWG). The aim is to connect the FESS at the DC bus level of a permanent magnet synchronous generator-based VSWG in order to stabilize the DC bus voltage as well as the power flowing into the grid. A rotor flux-oriented control strategy is proposed for the FESS converter based on the actual speed of the flywheel rotor. Since mechanical speed sensors are prone to failure and increase the maintenance cost of the system, a sensorless technique is proposed to estimate the rotor speed through a specially synthesized high-gain observer (HGO). The proposed observer achieves accurate tracking of the flywheel speed and flux and reduces the adverse effects of variations in the rated rotor resistance. Simulation results obtained using the MATLAB-Simulink environment are presented to illustrate the theoretical synthesis and analysis of the proposed FOC and sensorless HGO control strategies.

Keywords

IM FESS RFOC Sensorless control HGO 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Ali D, Asim M, Wallam F, Qazi Z, Abbas A, Naudhani Y (2019) Experimental testing of observers comprising discrete Kalman filter and high-gain observers. In: International conference on computer engineering & mathematical and engineering technology (iCoMET), pp 1–5Google Scholar
  2. Amranea A, Larabi A, Aitouche A (2018) Unknown input observer design for fault sensor estimation applied to induction machine. Math Comput Simul.  https://doi.org/10.1016/j.matcom.2018.09.018 MathSciNetCrossRefGoogle Scholar
  3. Arani AAK, Karami H, Gharehpetian GB, Hejazi MSA (2017) Review of flywheel energy storage systems structures and applications in power systems and microgrids. Renew Sustain Energy Rev 69:9–18CrossRefGoogle Scholar
  4. Bahloul M, Chrifi-Alaoui L, Dridd S, Souissi M, Chaabane M (2018) Robust sensorless vector control of an induction machine using multiobjective adaptive fuzzy Luenberger observer. ISA Trans 74:144–154CrossRefGoogle Scholar
  5. Beltran B, Benbouzid MEH, Ahmed-Ali T (2010) A combined high gain observer and high-order sliding mode controller for a DFIG-based wind turbine. In: IEEE international energy conference, pp 322–327Google Scholar
  6. Besbes M, Hadj Saïd S, M’Sahli F (2015) FPGA implementation of high gain observer for induction machine using Simulink HDL coder. In: International conference on control, engineering & information technologyGoogle Scholar
  7. Blasco-Gimenez R, Asher GM, Cilia J, Bradley KJ (1996) Field weakening at high and low speed for sensorless vector controlled induction machine. In: Proceedings of the IEEE international conference on PEVD, pp 258–261Google Scholar
  8. Boukettaya G, Krichen L (2014) A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications. Energy 71:148–159CrossRefGoogle Scholar
  9. Boumegoura T (2001) Electromagnetic signature detection of defects in an asynchronous machine and synthesis of observers for diagnosis, PhD thesis, l’école centrale de LyonGoogle Scholar
  10. Carnevale D, Possieri C, Tornambe A (2018) On over-sized high-gain practical observers for nonlinear systems. IEEE/CAA J Autom Sin 5(3):691–698MathSciNetCrossRefGoogle Scholar
  11. Chen J, Yao W, Zhang CK, Ren Y, Jiang L (2019) Design of robust MPPT controller for grid-connected PMSG-based wind turbine via perturbation observation based nonlinear control. Renew Energy 134:478–495CrossRefGoogle Scholar
  12. Cimuca GO, Saudemont C, Robyns B, Radulescu MM (2006) Control and performance evaluation of a flywheel energy-storage system associated to a variable-speed wind generator. IEEE Trans Ind Electron 53(4):1074–1085 CrossRefGoogle Scholar
  13. Debouza M, Al-Durra A, Errouissi R, Muyeen SM (2018) Direct power control for grid-connected doubly fed induction generator using disturbance observer based control. Renew Energy 125:365–372CrossRefGoogle Scholar
  14. Deng X, Yang J, Sun Y, Song D, Xiang XXG, Joo YH (2019) Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine. Energy 186:ID 115790CrossRefGoogle Scholar
  15. Dib A, Farza M, M’Saad M, Dorleans Ph, Massieu JF (2011) High gain observer for sensorless induction motor. In: International on federation of automatic control, Milano, ItalyGoogle Scholar
  16. Farza M, M’Saad M, Rossignol L (2004) Observer design for a class of MIMO nonlinear systems. Automatica 40:135–143MathSciNetCrossRefGoogle Scholar
  17. Gao J, Cao S, Zhang S (2018) The neural network control approach for PMSM based on a high gain observer. In: Youth academic annual conference of Chinese Association of automation (YAC), pp 1066–1071Google Scholar
  18. Ghanes M (2005) Observation and control of asynchronous machines without a mechanical sensor, PhD thesis, Université de NantesGoogle Scholar
  19. Ghosh S, Kamalasadan S (2017) An integrated dynamic modeling and adaptive controller approach for flywheel augmented DFIG based wind system. IEEE Trans Power Syst 32(3):2161–2171CrossRefGoogle Scholar
  20. Hamida MA, de Leon J, Glumineau A (2017) Experimental sensorless control for IPMSM by using integral backstepping strategy and adaptive high gain observer. Control Eng Pract 59:64–76CrossRefGoogle Scholar
  21. Hamzaoui I, Bouchafaa F, Talha A (2013) Comparative study between direct torque control and field-oriented control for induction machine used in flywheel energy storage system. J Electr Eng 13(7):1582–4594Google Scholar
  22. Hamzaoui I, Bouchafaa F, Talha A (2016) Advanced control for wind energy conversion systems with flywheel storage dedicated to improving the quality of energy. Int J Hydrog Energy 41:20832–20846CrossRefGoogle Scholar
  23. Hebner R, Beno J, Walls A (2002) Flywheel batteries come around again. IEEE Spectr 39(4):46–51 CrossRefGoogle Scholar
  24. Holtz J (2002) Sensorless control induction motor drives. Proc IEEE 90(8):1359–1394CrossRefGoogle Scholar
  25. Lawrence RG, Craven KL, Nichols GD (2003) Flywheel UPS. IEEE Ind Appl Mag 9(3):44–50CrossRefGoogle Scholar
  26. Leclercq L, Ansel A, Robyns B (2003) Autonomous high power variable speed wind generator system. In: Proceedings of the EPE, Toulouse, FranceGoogle Scholar
  27. Levi E, Wang M (1999) Main flux saturation compensation in sensorless vector controlled induction machines for operation in the field weakening region. In: Proceedings of the European power electronics conferenceGoogle Scholar
  28. Liaquat M, Javaid MA, Saad M (2017) A nonlinear high-gain observer for n-link robot manipulator which has measurement noise in a feedback control framework. In: International conference on control automation and systems (ICCAS), pp 755–759Google Scholar
  29. Liu R (2014) Sensorless speed estimation for long term flywheel energy storage system in standby mode. Ryerson University, Toronto MSC thesisGoogle Scholar
  30. Mansour M, Rachdi S, Mansouri MN, Mimouni MF (2012) Direct torque control strategy of an induction-machine-based flywheel energy storage system associated to a variable-speed wind generator. Energy Power Eng 4:255–263CrossRefGoogle Scholar
  31. Mansour M, Mansouri MN, Mimouni MF (2014) Modeling and control of IM-based flywheel energy-storage system associated to a variable-speed wind generator. In: 2nd International Conference on Renewable Energy (CIER’14), Monastir, TunisiaGoogle Scholar
  32. Miladi N, Dimassi H, Hadj Said S, M’sahli F (2018) Robust state estimation of a quadrotor based on high-gain and sliding-mode observers. In: International multi-conference on systems, signals & devices, pp 1166–1171Google Scholar
  33. Mousavi SM, Faraji F, Majazi A, Al-Haddad K (2017) A comprehensive review of flywheel energy storage system technology. Renew Sustain Energy Rev 67:477–490CrossRefGoogle Scholar
  34. Msaddek A, Gaaloul A, Msalhi F (2015) High gain observer based higher order sliding mode control: application to an induction motor. In: International multi-conference on systems, signals & devicesGoogle Scholar
  35. Nguyen TD, Tseng KJ, Zhang C, Zhang S, Nguyen HT (2010) Position sensorless control of a novel flywheel energy storage system, IPEC. Singapore, pp 1192–1198Google Scholar
  36. Prasad AR, Natarajan E (2006) Optimisation of integrated photovoltaic-wind power generation systems with battery storage. Energy 31:1943–54CrossRefGoogle Scholar
  37. Rossignol L, Farza M, Saad MM (2003) Nonlinear observation strategies for induction motors. In: IEEE international of electric machines and drives conference Madison, Wisconsin, USAGoogle Scholar
  38. Saudemont C, Robyns B, Cimuca G, Radulescu MM (2005) Grid connected or stand-alone real-time variable speed wind generator emulator associated to a flywheel energy storage system. In: Proceedings of the 11th European conference on power and electrical applicationsGoogle Scholar
  39. Song J, Lee KB, Song JH, Choy I, Kim KB (2000) Sensorless vector control of induction motor using a novel reduced-order extended Luenberger observer. In: IEEE Industry Applications Conference, ItalyGoogle Scholar
  40. Suzuki Y, Koyanagi A, Kobayashi M, Shimada R (2005) Novel applications of the flywheel energy storage system. Energy 30:2128–43CrossRefGoogle Scholar
  41. Treangle C, Farza M, M’Saad M (2017) A simple filtered high gain observer for a class of uncertain nonlinear systems. In: International conference on science & technology automation control & computer engineering (STA), pp 396–401Google Scholar
  42. Zhao H, Yao R, Xu L, Yuan Y, Li G, Deng W (2018) Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy. Entropy 20(9):682CrossRefGoogle Scholar
  43. Zhao H, Zheng J, Xu J, Deng W (2019) Fault diagnosis method based on principal component analysis and broad learning system. IEEE Access 7:99263–99272CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electrical Engineering Department, College of Engineering at YanbuTaibah UniversityYanbuSaudi Arabia
  2. 2.Research Unit of Industrial Systems Study and Renewable Energy, Electrical Engineering Department, National Engineering School of MonastirUniversity of MonastirMonastirTunisia
  3. 3.College of Computer Science and Engineering at YanbuTaibah UniversityYanbuSaudi Arabia

Personalised recommendations