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Photovoltaic Panel Parameters Estimation Using an Opposition Based Initialization Particle Swarm Optimization^{ †}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

_{ph}), reverse saturation current (Is), diode ideality factor (n), series resistance (Rs), and parallel resistance (R

_{sh}).

- -
- Analytical methods [7],
- -
- -

## 2. Photovoltaic Cell

#### 2.1. Characteristics of the PV Cell

#### 2.2. One Diode Model of PV Cell

## 3. Identification of PV Cell Parameters Using Optimization algorithms

#### 3.1. Optimization Algorithms

#### 3.2. Opposition Based Initialization Particle Swarm Optimization Technique

#### 3.3. ODM Parameters Extraction Using an IOB-PSO

Algorithm 1 IOBPSO |

1: Input: T, Ns, ${\mathrm{V}}_{\mathrm{Data}},{\mathrm{I}}_{\mathrm{Data}}$ 2: Output: global best 3: for each particle 4: Initialize particle position 5: Initialize particle velocity 6: Calculate cost value of particles using Equation (6) 7: Initialize opposite particle position using Equation(5) 8: Initialize opposite particle velocity 9: Calculate cost value of opposite particles using Equation (6) 10: If opposite particle cost < particle cost 11: Update particle 12: end if 13: Update particle best 14: If particle best cost < global best cost 15: Update global best 16: end if 17: end for 18: for (t =1: Max number of iterations) 19: for each particle 20: Update velocity using Equation (3) 21: Update position using Equation(4) 22: Calculate cost value using Equation (6) 23: If particle cost < particle best cost 24: Update particle best 25: If particle best cost < global best cost 26: Update global best 27: end if 28: end if 29: end for 30: end for 31: return global best 32: end procedure |

## 4. Test Results and Discussion

^{®}Core™ i5-2450M CPU processor @ 2.50GHz, 4GB RAM.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 5.**PSO with (

**a**) Random population initialization and (

**b**) Opposition based population initialization.

**Figure 9.**Convergence curve of the IOB-PSO for the (

**a**) STM6−40/36 module (

**b**) Photowatt-PWP201 module.

Parameters | Search Range |
---|---|

${\mathrm{I}}_{\mathrm{ph}}$ | [0.95 × ${\mathrm{I}}_{\mathrm{sc}}$, 1.05 × ${\mathrm{I}}_{\mathrm{sc}}$] |

${\mathrm{I}}_{\mathrm{s}}$ | [1 µA, 5 µA] |

$\mathrm{n}$ | [1, 2] |

${\mathrm{R}}_{\mathrm{sh}}$ | [$\frac{{\mathrm{V}}_{\mathrm{mpp}}}{{\mathrm{I}}_{\mathrm{sc}}-{\mathrm{I}}_{\mathrm{mpp}}}$, 1500 Ω] |

${\mathrm{R}}_{\mathrm{s}}$ | [0, $\frac{{\mathrm{V}}_{\mathrm{mpp}}-{\mathrm{V}}_{\mathrm{oc}}}{{\mathrm{I}}_{\mathrm{mpp}}}$] |

Parameters | Value |
---|---|

Cognitive factor c_{1} | 1.5 |

Social factor c_{2} | 2.0 |

Inertia weight w | [0.2, 0.9] |

Random values r_{1}, r_{2} | [0, 1] |

Number of particles | 30 |

Maximum iteration | 1000 |

Module | Type | ${\mathbf{N}}_{\mathbf{s}}$ | Temperature [°C] | $\mathbf{Irradiance}[\mathbf{w}/{\mathbf{m}}^{2}]$ |
---|---|---|---|---|

STM6-40/36 | Mono-crystalline | 36 | 51 | NA |

Photowatt-PWP201 | Poly-crystalline | 36 | 45 | 1000 |

Meth. | Parameters | Error | ||||
---|---|---|---|---|---|---|

${\mathbf{I}}_{\mathbf{p}\mathbf{h}}\left[\mathbf{A}\right]$ | ${\mathbf{I}}_{\mathbf{s}}\left[\mu \mathbf{A}\right]$ | n | ${\mathbf{R}}_{\mathbf{s}}\left[\mathbf{\Omega}\right]$ | ${\mathbf{R}}_{\mathbf{s}\mathbf{h}}\left[\mathbf{\Omega}\right]$ | RMSE | |

ABC [14] | 1.50 | 1.664 | 1.487 | 4.99 | 15.21 | 1.838 $\times {10}^{-3}$ |

CIABC [14] | 1.664 | 1.676 | 1.498 | 4.40 | 15.62 | 1.819 $\times {10}^{-3}$ |

CSA [13] | 1.664 | 2.000 | 1.534 | 2.91 | 15.841 | 1.794 $\times {10}^{-3}$ |

ImCSA [13] | 1.664 | 2.000 | 1.534 | 2.92 | 15.841 | 1.795 $\times {10}^{-3}$ |

IOB-PSO * | 1.663 | 2.88 | 1.57 | 0.0015 | 598.74 | 1.772 $\times {10}^{-3}$ |

Meth. | Parameters | Error | ||||
---|---|---|---|---|---|---|

${\mathbf{I}}_{\mathbf{p}\mathbf{h}}\left[\mathbf{A}\right]$ | ${\mathbf{I}}_{\mathbf{s}}\left[\mu \mathbf{A}\right]$ | n | ${\mathbf{R}}_{\mathbf{s}}\left[\mathbf{\Omega}\right]$ | ${\mathbf{R}}_{\mathbf{s}\mathbf{h}}\left[\mathbf{\Omega}\right]$ | RMSE | |

CPSO [13] | 1.0286 | 8.3010 | 1.451194 | 1.0755 | 1850.1 | 3.5 $\times {10}^{-3}$ |

PS [13] | 1.0313 | 3.1756 | 1.341358 | 1.2053 | 714.2857 | 1.18 $\times {10}^{-2}$ |

SA [13] | 1.0331 | 3.6642 | 1.356142 | 1.1989 | 833.3333 | 2.7 $\times {10}^{-3}$ |

CARO [15] | 1.03185 | 3.28401 | 1.35453 | 1.20556 | 841.3213 | 2.427 $\times {10}^{-3}$ |

IOB-PSO * | 1.030 | 3.495668 | 1.35 | 1.200877 | 986.306335 | 2.4251 $\times {10}^{-3}$ |

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**MDPI and ACS Style**

Touabi, C.; Ouadi, A.; Bentarzi, H.
Photovoltaic Panel Parameters Estimation Using an Opposition Based Initialization Particle Swarm Optimization. *Eng. Proc.* **2023**, *29*, 16.
https://doi.org/10.3390/engproc2023029016

**AMA Style**

Touabi C, Ouadi A, Bentarzi H.
Photovoltaic Panel Parameters Estimation Using an Opposition Based Initialization Particle Swarm Optimization. *Engineering Proceedings*. 2023; 29(1):16.
https://doi.org/10.3390/engproc2023029016

**Chicago/Turabian Style**

Touabi, Cilina, Abderrahmane Ouadi, and Hamid Bentarzi.
2023. "Photovoltaic Panel Parameters Estimation Using an Opposition Based Initialization Particle Swarm Optimization" *Engineering Proceedings* 29, no. 1: 16.
https://doi.org/10.3390/engproc2023029016