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An Enhanced Drift-Free Perturb and Observe Maximum Power Point Tracking Method Using Hybrid Metaheuristic Algorithm for a Solar Photovoltaic Power System

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

Despite of being a cleaner energy resource, the solar photovoltaic (SPV) system faces the dynamic and unpredictable changes in environmental conditions; hence, conventional and extant maximum power point tracking (MPPT) methods can get stuck at local minima. However, on account of less switching strain on the DC–DC converter, the drift-free P&O MPPT method can be supervised with effective bio-inspired metaheuristic algorithms to maximize its robustness and efficiency to generate photovoltaic power. Therefore, in this paper, the efficiency of the drift-free P&O MPPT method is significantly enhanced using a grey wolf skill embedded levy flight optimization (LI-GWO) method as a new approach. Firstly, a single-ended primary inductor converter (SEPIC)-based grid-connected SPV system is modeled to assess the MPPT performance. Further, using the LI-GWO enhanced drift-free P&O algorithm, the duty cycle of the SEPIC is regulated by updating the position of the grey wolfs based on the Brownian motion of the levy flights. Moreover, the exploration, exploitation and convergence analysis are carried out to examine the effectiveness of the proposed LI-GWO + drift-free P&O algorithm. In this manner, the proposed algorithm attains the global maxima quickly and, thereafter, the global MPP (GMPP) is tracked by the drift-free P&O itself with the less switching strain. The performance of the proposed MPPT approach is compared with the other conventional and hybrid metaheuristic-based MPPTs to show effectiveness under the newly formulated extreme weather condition model.

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Abbreviations

V oc :

Open-circuit voltage

i d,g :

d-Component of grid current

tilt:

Tilt angle

t e :

End time of the eclipse

t s :

Start time of the eclipse

r i :

Random numbers

r 1 , r 2 :

Randomly distributed numbers between [0 1]

W ss :

Sunset hour angle

w :

Inertial weight

r β, r δ :

Uniformly distributed random variables

γ :

Scale parameter

ζ :

Shift parameter

I o :

Saturation current

SPV:

Solar photovoltaic

P–V:

Power–voltage

PWM:

Pulse width modulation

APSO:

Accelerated particle swarm optimization

STC:

Standard test condition

GMPP:

Global maximum power point

MPP:

Maximum power point

PLL:

Phase lock loop

LFD:

Levy flight distribution

PSO-P&O:

Particle swarm optimization with perturb and observe

I-V:

Current–voltage

VSC:

Voltage source converter

GWO:

Grey wolf optimization

LI-GWO:

Levy-flight-inspired GWO

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Correspondence to Diwaker Pathak.

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The authors state that they have no known competing financial interests or personal ties that might have influenced the research presented in this study.

Appendix I

Appendix I

Combined flowchart for PSO and APSO-P&O MPPT techniques.

figure b

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Pathak, D., Katyal, A. & Gaur, P. An Enhanced Drift-Free Perturb and Observe Maximum Power Point Tracking Method Using Hybrid Metaheuristic Algorithm for a Solar Photovoltaic Power System. Iran J Sci Technol Trans Electr Eng (2023). https://doi.org/10.1007/s40998-023-00675-w

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  • DOI: https://doi.org/10.1007/s40998-023-00675-w

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