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

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

Abstract

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.

Keywords

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

P

Active power

Q

Reactive power

\(u_\mathrm{{g}}\)

Grid voltage

\(u_f\)

AC side voltage of the inverter

\(u_\mathrm{{dc}}\)

DC-link voltage

\(i_\mathrm{{g}}\)

Grid current

\(\hat{i}\)

Estimated current

S

Switching state of the inverter

\(T^{abc}\)

Coefficient matrix

R

Filter resistance

L

Filter inductance

\(\hat{L}\)

Estimated inductance

A

Upper bound factor

B

Lower bound factor

\(S_P\)

Hysteresis controller output for active power

\(S_Q\)

Hysteresis controller output for reactive power

\(\Theta \)

Angle of grid voltage

E

Finite set of estimated inductance candidates

J

Traditional cost function

\(J_N\)

Proposed cost function

\(J_L\)

Inductance estimation cost function

\(T_\mathrm{{s}}\)

Sampling period

\(T_\mathrm{{sim}}\)

Simulation step

\(T_{\mathrm{{r}},\mathrm{{desired}}}\)

Desired rising time of the inductance estimator

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

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

Abbreviations

MPPT

Maximum power point tracking

SVM

Space vector modulation

VOC

Voltage oriented control

LT-DPC

Lookup table direct power control

T-FS-MPPC

Traditional finite-set model predictive power control

CE-FS-MPPC

Computationally efficient finite-set model predictive power control

UKF

Unscented Kalman filter

Notes

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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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