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Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic^{ †}

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## Abstract

**:**

## 1. Introduction

- Using of Fuzzy logic controller as MPPT system optimized by GA and PSO solvers;
- Using GA for design the architecture of ANN-based MPPT;
- Comparison between these two AI-based methods;
- Proposition a combination of the two methods because each of them is better for a certain range of irradiance and temperature;
- The results are elaborated and comparisons with incremental conductance and perturb and observe methods are presented;
- The comparisons are presented for both linear and step variations of irradiance and temperature.

## 2. Methods of Maximum Power Point Tracking

#### 2.1. PV Array Modeling

#### 2.2. Conventional Methods

#### 2.3. Artificial Intelligence Methods for MPPT

#### 2.3.1. GA/PSO Fuzzy Logic MPPT

#### 2.3.2. GA-ANN for MPPT

## 3. Application of the Artificial Intelligence Methods for MPPT

#### 3.1. Application of GA/PSO-FLC Based MPPT Method

#### 3.2. Application of GA-ANN MPPT Method

#### 3.3. Comparison of GA/PSO-FLC and GA-ANN Based MPPT

#### 3.4. Dynamic Environmental Conditions Test of the AI Based Methods

#### 3.5. Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**The change of the maximum power point of the PV panel (SUNPOWER 305) for the variation of: (

**a**) solar irradiance at ${T}_{c}={25}^{\phantom{\rule{3.33333pt}{0ex}}\circ}$C; (

**b**) cell temperature at $G=1000$ W/m${}^{2}$.

**Figure 4.**The membership functions of the fuzzy logic control (FLC) inputs (E and $\Delta E$) and output ($\Delta D$).

**Figure 6.**The 25 fuzzy rules (If-Then rules), which relates the inputs and the outputs of FLC system.

**Figure 7.**The proposed design of the artificial neural network (ANN) used for maximum power point tracking (MPPT).

**Figure 11.**A comparison of the output DC power of the PV array using genetic algorithm (GA)-FLC, particle swarm optimization (PSO)-FLC, perturb-and-observe (P&O) and incremental conductance (INC) for; (

**a**) linear variation of G and ${T}_{c}$ (

**b**) step variation of G and ${T}_{c}$.

**Figure 12.**The output DC power difference when changing the optimized gains with $\pm 10\%$ of: (

**a**) GA-FLC based MPPT method (

**b**) PSO-FLC based MPPT method for linear variation of G and ${T}_{c}$.

**Figure 13.**A comparison of the output DC power of the PV array using GA-ANN, P&O and INC for; (

**a**) linear variation of G and ${T}_{c}$ (

**b**) step variation of G and ${T}_{c}$.

**Figure 14.**Proximate views of the output DC power of the PV array when using the GA-FLC and the GA-ANN based MPPT methods for linear variations of G and ${T}_{c}$: (

**a**) from 0.1 to 0.3 s; (

**b**) from 0.914 to 0.922 s; (

**c**) from 0.9 to 1.5 s; (

**d**) from 1.6 to 1.7 s.

**Figure 15.**Proximate views of the output DC power of the PV array when using the GA-FLC and the GA-ANN based MPPT methods for step variations of G and ${T}_{c}$: (

**a**) from 0.1 to 0.2 s; (

**b**) from 0.75 to 0.79 s; (

**c**) from 0.784 to 0.8 s; (

**d**) from 1.515 to 1.525 s.

**Figure 16.**Close views of combining the output DC power response from the GA-FLC and the GA-ANN based MPPT methods based on the environmental conditions G and ${T}_{c}$ for their linear variations: (

**a**) from 0.1 to 0.3 s; (

**b**) from 0.914 to 0.922 s; (

**c**) from 0.9 to 1.5 s; (

**d**) from 1.6 to 1.7 s.

**Figure 17.**Close views of combining the output DC power response from the GA-FLC and the GA-ANN based MPPT methods based on the environmental conditions G and ${T}_{c}$ for their step variations (

**a**) from 0.1 to 0.2 s; (

**b**) from 0.75 to 0.79 s; (

**c**) from 0.784 to 0.8 s; (

**d**) from 1.515 to 1.525 s.

**Figure 19.**A Comparison of the output DC power of the PV array using GA-FLC, PSO-FLC, P&O and INC MPPT methods for a dynamic irradiance change based on EN50530 standard.

**Figure 20.**A Comparison of the output DC power of the PV array using GA-ANN, P&O and INC MPPT methods for a dynamic irradiance change based on EN50530 standard.

**Figure 21.**Proximate views of combining the responses of the output DC power from the GA-FLC and the GA-ANN based MPPT methods for the dynamic solar irradiance change (

**a**) from 0.1 to 0.3 s; (

**b**) from 0.71 to 0.8 s.

$\mathbf{\Delta}\mathit{E}$ E | NB | NS | ZE | PS | PB |
---|---|---|---|---|---|

NB | ZE | ZE | PB | PB | PB |

NS | ZE | ZE | PS | PS | PS |

ZE | PS | ZE | ZE | ZE | NS |

PS | NS | NS | NS | ZE | ZE |

PB | NB | NB | NB | ZE | ZE |

**Table 2.**A quantitative evaluation of the proposed MPPT methods in terms of the produced energy and the rise time.

Step Variations of G and ${\mathit{T}}_{\mathit{c}}$ | Ramp Variations of G and ${\mathit{T}}_{\mathit{c}}$ | ||
---|---|---|---|

Output Energy (KJ) | Rise Time (s) | Output Energy (KJ) | |

INC | 141.92 | 0.0251 | 127.52 |

P&O | 141.95 | 0.0239 | 127.54 |

GA-FLC | 147.27 | 0.0193 | 129.43 |

PSO-FLC | 147.26 | 0.0193 | 129.43 |

GA-ANN | 147.17 | 0.0169 | 129.31 |

COMBINED GA-FLC-ANN | 147.34 | 0.0168 | 129.44 |

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**MDPI and ACS Style**

Ali, M.N.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F.
Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic. *Sensors* **2021**, *21*, 1244.
https://doi.org/10.3390/s21041244

**AMA Style**

Ali MN, Mahmoud K, Lehtonen M, Darwish MMF.
Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic. *Sensors*. 2021; 21(4):1244.
https://doi.org/10.3390/s21041244

**Chicago/Turabian Style**

Ali, Mahmoud N., Karar Mahmoud, Matti Lehtonen, and Mohamed M. F. Darwish.
2021. "Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic" *Sensors* 21, no. 4: 1244.
https://doi.org/10.3390/s21041244