Efficient model predictive power control with online inductance estimation for photovoltaic inverters

  • Issa HammoudEmail author
  • Khaled Morsy
  • Mohamed Abdelrahem
  • Ralph Kennel
Original Paper


In this paper, a computationally efficient finite-set model predictive power control for grid-connected photovoltaic systems combined with a novel online finite-set model inductance estimation technique is proposed. The proposed control scheme overcomes the well-known challenges associated with predictive control in power electronics applications, which are: high model dependency and short sampling periods. The reference voltage vector (VV) of the grid-connected inverter that will enhance the desired power flow can be computed analytically with the knowledge of the reference and actual measured power values. Based on its location in the \(\alpha \)\(\beta \) reference frame, a finite set of three candidates instead of seven is evaluated to select the optimal VV. Furthermore, the performance of the proposed scheme is compared with the traditional finite-set model predictive power control, voltage oriented control with PI controllers, lookup table direct power control. Finally, the novel online inductance estimation technique is described and compared with unscented Kalman filter.


Model predictive power control Voltage oriented control Lookup table direct power control Finite set model inductance estimation Unscented Kalman filter Photovoltaic systems 

List of symbols


Active power


Reactive power


Grid voltage


AC side voltage of the inverter


DC-link voltage


Grid current


Estimated current


Switching state of the inverter


Coefficient matrix


Filter resistance


Filter inductance


Estimated inductance


Upper bound factor


Lower bound factor


Hysteresis controller output for active power


Hysteresis controller output for reactive power

\(\Theta \)

Angle of grid voltage


Finite set of estimated inductance candidates


Traditional cost function


Proposed cost function


Inductance estimation cost function


Sampling period


Simulation step


Desired rising time of the inductance estimator

\(\Delta { \hat{L}_\mathrm{{desired}}}\)

Desired estimated inductance change within \(T_{\mathrm{{r}},\mathrm{{desired}}}\)



Maximum power point tracking


Space vector modulation


Voltage oriented control


Lookup table direct power control


Traditional finite-set model predictive power control


Computationally efficient finite-set model predictive power control


Unscented Kalman filter



  1. 1.
    Zervos A (2018) REN21, renewables 2018 global status report , ParisGoogle Scholar
  2. 2.
    Kuhn P, Huber M, Dorfner J, Hamacher T (2015) Challenges and opportunities of power systems from smart homes to super-grids. Ambio 45(S1):50–62CrossRefGoogle Scholar
  3. 3.
    Jäger-Waldau A (2016) PV status report, European Commission Joint Research CentreGoogle Scholar
  4. 4.
    Kuik O, Branger F, Quirion P (2019) Competitive advantage in the renewable energy industry: evidence from a gravity model. Renew Energy 131:472–481CrossRefGoogle Scholar
  5. 5.
    Philipps S, Warmuth W (2017) Photovoltaics report. Fraunhofer Institute for Solar Energy Systems (ISE), with support of PSE AG, FreiburgGoogle Scholar
  6. 6.
    Philipps S, Warmuth W (2019) Photovoltaics report. Fraunhofer Institute for Solar Energy Systems (ISE), with support of PSE AG, FreiburgGoogle Scholar
  7. 7.
    Luceno Sanchez J, Diez Pascual A, Capilla R (2019) Materials for photovoltaics: state of art and recent developments. Int J Mol Sci 20(4):976CrossRefGoogle Scholar
  8. 8.
    Zhao T, Zong Q, Zhang T, Xu Y (2016) Study of photovoltaic three-phase grid-connected inverter based on the grid voltage-oriented control. In: 2016 IEEE 11th conference on industrial electronics and applications (ICIEA), Hefei, pp 2055–2060Google Scholar
  9. 9.
    Islam KA, Abdelrahem M, Kennel R (2016) Efficient finite control set-model predictive control for grid-connected photovoltaic inverters. In: 2016 international symposium on industrial electronics (INDEL), Banja LukaGoogle Scholar
  10. 10.
    Ben Youssef F, Sbita L (2017) Sliding mode control strategy for grid connected PV system. In: 2017 international conference on green energy conversion systems (GECS), Hammamet, Tunisia, pp 1–7Google Scholar
  11. 11.
    Alonso-Martínez J, Carrasco JEG, Arnaltes S (2010) Table-based direct power control: a critical review for microgrid applications. IEEE Trans Power Electron 25(12):2949–2961CrossRefGoogle Scholar
  12. 12.
    Rodríguez J, Kazmierkowski M, Espinoza J, Zanchetta P, Abu-Rub H, Young H, Rojas C (2013) State of the art of finite control set model predictive control in power electronics. IEEE Trans Ind Inform 9(2):1003–1016CrossRefGoogle Scholar
  13. 13.
    Abdelrahem M, Hackl C, Kennel R (2017) Finite set model predictive control with on-line parameter estimation for active frond-end converters. Electr Eng. access) CrossRefGoogle Scholar
  14. 14.
    Zhi D, Xu L, Williams BW (2009) Improved direct power control of grid-connected DC/AC converters. IEEE Trans Power Electron 24(5):1280–1292CrossRefGoogle Scholar
  15. 15.
    Zhang Y, Peng Y, Qu C (2016) Model predictive control and direct power control for PWM rectifiers with active power ripple minimization. IEEE Trans Ind Appl 52(6):4909–4918CrossRefGoogle Scholar
  16. 16.
    Geyer T, Scoltock J, Madawala U (2011) Model predictive direct power control for grid-connected converters. In: IECON 2011—37th annual conference of the IEEE industrial electronics society. Melbourne, VIC, pp 1438–1443Google Scholar
  17. 17.
    Kennel R, Linder A, Linke M (2001) Generalized predictive control (GPC)-ready for use in drive applications? In: IEEE 32nd annual power electronics specialists conferenceGoogle Scholar
  18. 18.
    Cortés P, Rodríguez J, Antoniewicz P, Kazmierkowski M (2008) Direct power control of an AFE using predictive control. IEEE Trans Power Electron 23(5):2516–2523CrossRefGoogle Scholar
  19. 19.
    Rodríguez J, Cortés P (2012) Predictive control of power converters and electrical drives, 1st edn. Wiley, New YorkCrossRefGoogle Scholar
  20. 20.
    Young H, Perez M, Rodriguez J (2016) Analysis of finite-control-set model predictive current control with model parameter mismatch in a three-phase inverter. IEEE Trans Ind Electron 63(5):3100–3107CrossRefGoogle Scholar
  21. 21.
    Hammoud I, Morsy K, Abdelrahem M, Kennel R (2019) Computationally efficient model predictive direct power control with online finite set model inductance estimation technique for grid-connected photovoltaic inverters. In: IEEE international symposium on predictive control of electrical drives and power electronics (PRECEDE).
  22. 22.
    Dannehl J, Wessels C, Fuchs FW (2009) Limitations of voltage-oriented PI current control of grid-connected PWM rectifiers with LCL filters. IEEE Trans Ind Electron 56(2):380–388CrossRefGoogle Scholar
  23. 23.
    Noguchi T, Tomiki H, Kondo S, Takahashi I (1998) Direct power control of PWM converter without power-source voltage sensors. IEEE Trans Ind Appl 34(3):473–479CrossRefGoogle Scholar
  24. 24.
    Lee SS, Heng YE (2016) Predictive direct power control of multilevel direct current link converter for grid connected battery energy storage systems. J Renew Sustain Energy 8(3):034104MathSciNetCrossRefGoogle Scholar
  25. 25.
    Liu X, Wang D, Peng Z (2016) Predictive direct power control for three-phase grid-connected converters with online parameter identification. Int Trans Electr Energy Syst. CrossRefGoogle Scholar
  26. 26.
    Abdelrahem M, Hackl C, Kennel R (2015) Application of extended Kalman filter to parameter estimation of doubly-fed induction generators in variable-speed wind turbine systems. In: 2015 international conference on clean electrical power (ICCEP), Taormina, pp 226–233Google Scholar
  27. 27.
    Abdelrahem M, Hackl C, Kennel R (2017) Simplified model predictive current control without mechanical sensors for variable-speed wind energy conversion systems. Electr Eng J 99(1):367–377CrossRefGoogle Scholar
  28. 28.
    Stati N, Abdelrahem M, Mobarak MH, Kennel R (2016) Finite control set-model predictive control with on-line parameter estimation for variable-speed wind energy conversion systems. In: IEEE international symposium on industrial electronics (INDEL), Banja Luka, pp 1–6Google Scholar
  29. 29.
    Mehreganfar M, Davari SA (2017) Sensorless predictive control method of three-phase AFE rectifier with MRAS observer for robust control. In: IEEE international symposium on predictive control of electrical drives and power electronics (PRECEDE), Pilsen, pp 107–112Google Scholar
  30. 30.
    Wan EA, Van Der Merwe R (2000) The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 adaptive systems for signal processing, communications, and control symposium, pp 153–158Google Scholar
  31. 31.
    Tsili M, Papathanassiou S (2009) A review of grid code technical requirements for wind farms. IET Renew Power Gener 3(3):308–332CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Institute for Electrical Drive Systems and Power ElectronicsTechnical University of Munich (TUM)MunichGermany
  2. 2.Powertrain Mechatronics, Control Engineering Excellence ClusterIAV GmbHGifhornGermany
  3. 3.Power Electronics DesignBMW GroupMunichGermany
  4. 4.Electrical Engineering Department, Faculty of EngineeringAssiut UniversityAssiutEgypt

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