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Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm

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

Abstract

Solar cell is one of the important renewable energy resources, and it is considered a promising source for energy challenges in the future. The identification of solar cell model parameters is very important due to the control and the simulation of PV systems. In this paper, an enhanced teaching–learning-based optimization (ETLBO) algorithm is proposed and applied to estimate the photovoltaic cells parameter. The ETLBO is proposed to improve the performance of conventional TLBO and reduce its search space by adjusting the parameters which control the explorative and exploitative phases to achieve the suitable balancing. The proposed algorithm is validated using real dataset of photovoltaic single-diode and double-diode models. In addition, the proposed algorithm is tested on the dataset of two real PV panels (polycrystalline and monocrystalline). The results obtained by the proposed algorithm are compared with those obtained by other well-known optimization algorithms. All results prove the effectiveness and superiority of proposed algorithm compared with other optimization techniques.

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Abbreviations

I ph :

Photogenerated current source

R S :

Series resistance

R sh :

Shunt resistance

I t :

PV module output current

q :

1.602 × 10−19 (C) Coulombs

I d1 :

First diode current

n 1 :

Diffusion diode ideality factor

I tm :

Real dataset current

T F :

Teaching factor

I sd :

Diode rectifier current

I sh :

Shunt resistance current

V t :

Terminal voltage

T (K°):

Photocell temperature (kelvin)

K :

1.380 × 10−23 (J/K°) Boltzmann constant

I d2 :

Second diode current

n 2 :

Recombination diode ideality factor

I te :

Estimated current

x :

Population matrix

rand:

Uniformly distributed random values within range (0, 1)

PGJAYA:

Performance-guided JAYA algorithm

BFO:

Bacterial foraging optimization

TLABC:

Teaching–learning-based artificial bee colony

ELPSO:

Enhanced leader particle swarm optimization

TVACPSO:

Time-varying acceleration coefficients particle swarm optimization

TLBO:

Teaching–learning-based optimization

SCA:

Sine–cosine algorithm

ETLBO:

Enhanced TLBO using SCA

RMSE:

Root-mean-square error

GOTLBO:

Generalized oppositional teaching–learning-based optimization

BHCS:

Biogeography-based heterogeneous cuckoo search

SATLBO:

Self-adaptive TLBO

WDO:

Wind-driven optimization

GSA:

Gravitational search algorithm with linearly decreasing gravitational constant

SD:

Single diode

DD:

Double diode

PV:

Photovoltaic

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Acknowledgements

The authors gratefully acknowledge the contribution of the NSFC (China)-ASRT (Egypt) Joint Research Fund, Project No. 51861145406 for providing partial research funding to the work reported in this research.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswân, 81542, Egypt

    Abdelhady Ramadan, Salah Kamel & Ahmed Korashy

  2. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing, 400030, China

    Salah Kamel & Juan Yu

Authors
  1. Abdelhady Ramadan
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  2. Salah Kamel
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  3. Ahmed Korashy
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  4. Juan Yu
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Correspondence to Salah Kamel.

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Ramadan, A., Kamel, S., Korashy, A. et al. Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm. Iran J Sci Technol Trans Electr Eng 44, 767–779 (2020). https://doi.org/10.1007/s40998-019-00257-9

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  • DOI: https://doi.org/10.1007/s40998-019-00257-9

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