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An in-depth study of robust MPPT for extend optimal power extraction using wind speed compensation technique of wind generators

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Abstract

To achieve effective power production and control, it is essential to establish an effective maximum power point tracking (MPPT) approach. This makes designing a wind turbine easier. A brand new method called robust changeable step-perturb & observe (RVS-P&O) is created to address the issues of wind speed variable situations, significant oscillations around the maximum power point (MPP), and step size estimate all at once. Where two main points are involved, it is first suggested to choose step sizes systematically based on the normalization of power and speed data. Second, a novel adjustment to compute the power variation is made in order to provide adequate resilience for large and prolonged wind speed fluctuations. According to the simulation findings, the suggested RVS-P&O-based MPPT approach is better to the competing P&O techniques, variable step-P&O, small fixed step-P&O, and large fixed step-P&O. An improvement of 1.34% over the variable step-P&O algorithm is provided by the suggested RVS-P&O approach, which delivers a WECS efficiency of 99.05%, when compared to the 0.032 s, 0.071 s, 0.018 s, 0.012 s, and 0.007 s provided by LS-P&O, SS-P&O, VS-P&O, ME-PO, and AD-P&O, respectively. The settling time for V = 6 m/s is really noticeably improved; it is now 0.004 s. As a result, the RVS-P&O algorithm could be a good choice for MPP online operation monitoring in terms of energy efficiency, transient- and steady-state regime performances under diverse operating situations, and multiple data for wind speed.

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Data availability statement

The data that support the findings of this study are available from the corresponding author, [MEGHNI BILLEL], upon reasonable request.

Abbreviations

\({C}_{\mathrm{P}}\) :

Coefficient power

F :

Simplex

\({f}_{\mathrm{g}}\) :

Grid frequency

\({I}_{\mathrm{d}}\) :

d-axis current

\({I}_{\mathrm{dg}}\) :

Grid d-axis current

\({I}_{\mathrm{q}}\) :

q-axis current

\({I}_{\mathrm{qg}}\) :

Grid q-axis current

\(K\) :

First unknown gain

l :

Sector index

\({L}_{\mathrm{d}}\) :

d-axis inductance

\({L}_{\mathrm{q}}\) :

q-axis inductance

\(M\) :

Second unknown gain

m :

Complex size

N :

Normalization index

P :

Number of independent complexes

\({P}_{\mathrm{g}}\) :

Grid active power

\({P}_{t}\) :

Power of the air mass

\({P}_{k}\) :

Turbine power

\({Q}_{\mathrm{g}}\) :

Grid reactive power

\({R}_{\mathrm{s}}\) :

Stator resistance

\(S\) :

Surface

\({S}_{\mathrm{P}}\) :

Sliding surface of the active power

\({s}_{Q}\) :

Sliding surface of the reactive power

\({T}_{\mathrm{e}}\) :

Electromagnetic torque

\({\alpha }_{L}\) :

Weighting factor

V :

Wind speed

\({V}_{\mathrm{d}}\) :

d-axis voltage

\({V}_{\mathrm{dg}}\) :

Grid d-axis voltage

\({V}_{\mathrm{di}}\) :

Inverter d-axis voltage

\({V}_{\mathrm{q}}\) :

q-axis voltage

\({V}_{\mathrm{qg}}\) :

Grid q-axis voltage

\({V}_{\mathrm{qi}}\) :

Inverter q-axis voltage

\(W\) :

Selection factor

d :

Stator axis

e :

Electromagnetic

f :

Flux

g :

Grid

i :

Element (solution)

j :

Point index

k :

Complex index

max:

Maximum

mes:

Measure

\(\mathrm{opti}\) :

Optimum

p :

Power

q :

Stator axis

ref:

Reference

s :

Stator

α :

Number of iteration for each simplex

β :

Blade pitch angle

λ :

Tip speed ratio

\(\rho \) :

Air density

τ :

Number of offspring

\(\omega \) :

Electric pulsation

\({\uppsi }_{\mathrm{f}}\) :

Magnetic flux

AC:

Alternate current

AI:

Artificial intelligent

ANFIS:

Adaptive neuro fuzzy inference system

ANN:

Artificial neural network

DC:

Direct current

DFIG:

Doubly fed induction generator

DPC:

Direct power control

FLC:

Fuzzy logic control

FOSMC:

First-order sliding mode control

FOC:

Field-oriented control

FS:

Fixed

GSC:

Grid side converter

INC:

Incremental conductance

IPC:

Indirect power controller

LS:

Large step

MPPT:

Maximum power point tracking

MSC:

Machine side converter

ORB:

Optimum relation-based

ORC:

Optimal rotational cycle

OTC:

Optimal torque control

P&O:

Perturb & observe

PMSG:

Permanent magnet synchronous generator

PSO:

Particle swarm optimizer

PSF:

Power signal feedback

RVS:

Robust variable step

SCIG:

Squirrel cage induction generator

SS:

Small step

SOSMC:

Second-order sliding mode control

STA:

Super-twisting algorithm

SVM:

Support vector machine

SVPWM:

Space vector pulse width modulation

THD:

Transanal hemorrhoidal dearterialization

VS:

Variable step

VSWT:

Variable speed wind turbine

WECS:

Wind energy control system

WSE:

Wind speed estimated

WT:

Wind turbine

VDC ref:

Reference of DC link voltage

Pg ref:

Reference of grid active power

Qg ref:

Reference of grid reactive power

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Acknowledgements

In their acknowledgements, the writers mention the “LSEM Laboratory, University Badji Mokhtar, Annaba, Algeria).”

Funding

The authors received no specific funding for this study.

Author information

Authors and Affiliations

  1. LSEM Laboratory, Department of Electrical Engineering, University Badji Mokhtar, Annaba, Algeria

    Meghni Billel

  2. Laboratoire LAGE, Department of Electronics and Telecommunications, Faculty of New Technologies of Computing and Communication, University of Ouargla, 30000, Ouargla, Algeria

    Toumi Ilham

  3. L.E.R.I.C.A., Department of Electronics, University of Badji Mokhtar, P.O. Box 12, 23000, Annaba, Algeria

    Boulmaiz Amira

  4. Department of Drilling and Rig Mechanics, Faculty of Hydrocarbons, Renewable Energies, and Earth and Universe Sciences, University of Ouargla, 30000, Ouargla, Algeria

    Hachana Oussama

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  1. Meghni Billel
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  4. Hachana Oussama
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Correspondence to Meghni Billel.

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Appendix

Appendix

See Tables 6, 7, 8.

Table 6 PMSG setting parameters
Full size table
Table 7 WT setting parameters
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Table 8 DC bus and Grid setting parameters
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Billel, M., Ilham, T., Amira, B. et al. An in-depth study of robust MPPT for extend optimal power extraction using wind speed compensation technique of wind generators. Electr Eng 105, 681–704 (2023). https://doi.org/10.1007/s00202-022-01691-5

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