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Model predictive control and ANN-based MPPT for a multi-level grid-connected photovoltaic inverter

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Abstract

This paper deals with the control of a five-level grid-connected photovoltaic inverter. Model Predictive Control is applied for controlling active and reactive powers injected into the grid. The operation of the photovoltaic field at the maximum power point is ensured using an algorithm based on a neural network. Model Predictive Control is based on the choice of inverter state by minimizing a cost function that depends on active and reactive powers. In addition, the redundant states of the inverter are directly utilized to balance the bus voltages of the inverter, making overall system control simple and efficient.

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Abbreviations

A :

P–n junction ideality factor, between 1 and 5

C 1234 :

Dc link capacitances

E g :

Bang-gap energy of the semiconductor used in the cell

e a,b,c :

Three phase voltages of the grid

e d,q :

D-q components of the grid voltage

i a,b,c :

Three phase currents of the grid

i c 1234 :

Currents in dc link capacitors

i d,q :

D-q components of the grid current

i d 12345 :

Input currents of the five level inverter

i ph :

Cell’s photocurrent

i pv :

Output current of PV array

i RS :

Cell’s reverse saturation current at reference temperature and solar irradiation

i S :

Cell’s reverse saturation current

I N :

Output current of the PV array at the Nth sample of time.

I N −1 :

Output current of the PV array at the (N − 1)th sample of time.

k :

Boltzman’s constant, 1.380658 × 10–23 j/K

k i :

Cell’s short-circuit current temperature coefficient

L :

Line inductance of the grid

N p :

Number of panels connected in parallel

N s :

Number of panels connected in series

q :

Electron charge, 1.60217733 × 10–19 Cb

q ij :

Switching signals of the inverter transistors

R :

Line resistance of the grid

R s :

Cell series resistance

S :

Total solar irradiation, W/m2

T c :

Cell’s absolute working temperature, K

T ref :

Cell’s reference temperature, K

T s :

Sampling time used in MPC control

v c 1234 :

Voltages of dc-link capacitors

V N :

Output voltage of the PV array at the Nth sample of time

V N −1 :

Output voltage of the PV array at the (N − 1)th sample of time

V s :

Output voltage vector of the inverter

v a,b,c :

Three phase output voltages of the inverter

v dc :

Output voltage of PV array

v d,q :

D-q components of the output voltage of the inverter

α :

Scaling factor for adjusting the step size of incremental conductance MPPT

ω :

Frequency of grid voltages

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Acknowledgements

We would like to thank the Algerian DGRSDT for funding our research through the PRFU research projects of codes A01L07UN180120190003 and A01L07UN180120190005.

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Authors and Affiliations

  1. Faculty of Sciences and Technology, Renewable Energy Laboratory, Jijel University, PO Box 98, Jijel, Algeria

    Hemza Bouaouaou, Djaafer Lalili & Nasserdine Boudjerda

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  1. Hemza Bouaouaou
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  2. Djaafer Lalili
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  3. Nasserdine Boudjerda
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Correspondence to Djaafer Lalili.

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Appendix

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Bouaouaou, H., Lalili, D. & Boudjerda, N. Model predictive control and ANN-based MPPT for a multi-level grid-connected photovoltaic inverter. Electr Eng 104, 1229–1246 (2022). https://doi.org/10.1007/s00202-021-01355-w

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