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An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

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Abstract

The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%.

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Abbreviations

GMPP:

Global maximum power point

MPP:

Maximum power point

MPPT:

Maximum power point tracking

CSA:

Crow search algorithm

PSO:

Particle swarm optimization

PSC:

Partial shading case

P&O:

Perturb and observe

FPA:

Flower pollination algorithm

ELPSO-P&O:

Enhanced leader PSO-P&O

FLC:

Fuzzy logic control

PV:

Photovoltaic

q :

The charge of the electron (C)

T :

Absolute temperature (°K)

k :

Boltzmann constant (J/K)

v :

Particle velocity (speed)

V :

Lyapunov function

D :

Duty cycle

ΔD :

Step size of the duty cycle

\(D_{ibest}^{k}\) :

Best current duty cycle at iteration k

\(D_{gbest}\) :

Best global duty cycle

\(P_{i}^{k}\) :

PV power at iteration k for the crow i

c 1 , c 2 :

Acceleration coefficients

r :

Random number

w :

Inertial weight

\(x_{i}^{k}\) :

Position of crow i at iteration k

\(m_{j}^{k}\) :

Memory of crow j at iteration k

N :

Number of crows in the flock

fl :

Is the flight length

AP :

Awareness probability

k :

Iteration

\(P_{\,\max }^{k}\) :

Maximum PV power at iteration k

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Authors and Affiliations

  1. Electrical Engineering Department, Laboratory LGEB, University of Biskra, Biskra, Algeria

    Yehya Houam & Amel Terki

  2. Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement Des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria

    Yehya Houam & Noureddine Bouarroudj

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  1. Yehya Houam
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Houam, Y., Terki, A. & Bouarroudj, N. An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA). J. Electr. Eng. Technol. 16, 381–402 (2021). https://doi.org/10.1007/s42835-020-00590-8

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