# Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

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## Abstract

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## 1. Introduction

- What are the most commonly used types of ANN architecture to calculate the MPPT of a PV system?
- Are existing ANN algorithms suitable for calculating the MPP of a PV system?

## 2. Theoretical Bases

#### 2.1. Artificial Neural Networks System

#### 2.2. ANN Architecture

#### 2.3. Fuzzy Logic System

#### 2.4. Photovoltaic Solar Energy Systems

## 3. Artificial Neural Networks for MPPT Control in PV Systems

- Algorithms using ANN.
- Algorithms using ANN + FL.
- Hybrid algorithms (ANN plus metahuristic or optimization algoritms).

## 4. MPPT Control Using ANN Plus FL

## 5. MPPT Control Using ANN and Hybrid Metaherustic Algorithms

## 6. Discussion

- Create a methodological basis for the analysis of the control of PV systems and their architectures;
- Detail some of the classical and advanced control algorithms that may be used in them;
- Enable the to implement the acquired knowledge in a real environment.

## 7. Conclusions

- Ant lions algorithm
- BAT algorithm
- Black Widow Algorithm
- Dragonfly Algorithm
- Grasshopper Optimization Algorithm
- Moth Flame Optimization Algorithm
- Multiversal Optimization
- Salp Swarm Optimization Algorithm

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Sample Availability

## Abbreviations

AI | Artificial Intelligence |

ANN | Artificial Neural Networks |

AC | Alternating Current |

ANFIS | Adaptive Neuro-Fuzzy Inference System |

DC | Direct Current |

EANFIS | Enhanced Adaptive Neuro-Fuzzy Inference System |

FL | Fuzzy Logic |

GMPP | Global Maximum Power Point |

InC | Incremental Conductance |

PV | Photovoltaic |

PVC | Photovoltaics Cell |

PVS | Photovoltaic System |

P&O | Perturb and Observation |

PSO | Particle Swarm Optimization |

PSC | Partial Shading Condition |

MPP | Maximum Power Point |

MPPT | Maximum Power Point Tracking |

RE | Renewable Eneregy |

SAPV | Stand-Alone Photovoltaic |

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References | Network Architecture | Efficiency % | Conditions | Error | Input Variables |
---|---|---|---|---|---|

[57] | multilayer feedforward | 99.6% | variable | – | G and T |

[58] | backpropagation momentum | 98.47% | variable | – | V and I |

[59] | multilayer feedforward | – | uniform | – | V, I, G, T |

and variable | |||||

[60] | Rprop-NN | – | variable | – | G and T |

[61] | Recurrent exogenous (NARX) | – | partial shading | – | V and T |

[62] | single-layer | – | uniform | 0.0612 | G and T |

[63] | RHONN | 93% | variable | 0.3131 | – |

[64] | feedforward three layers | 98.77% | uniform | – | V and I |

and partial shading | |||||

[65] | multilayerd feedforward | – | variable | 0.094115 | power derivate (dP) |

and voltage derivate (dV) | |||||

[66] | feedforward | 97% | variable | 2.1961 | G and T |

[67] | NARX | – | variable | 0.0159 | G and T |

References | Number of Rules | Type | Efficiency% of MPPT Controller | Fuzzy Inference System | Network Architecture |
---|---|---|---|---|---|

[71] | 9 | ANFIS | 80% | Sugeno Model | – |

[72] | 6 | ANFIS | – | Sugeno Model | feedforward |

[73] | 42 | ANFIS | – | Sugeno Model | – |

[74] | 49 | ANFIS | 99.88% | Sugeno Model | – |

[75] | 15 | ANFIS | 99.3% | Sugeno Model | backpropagation |

[76] | 19 | ANFIS | – | Sugeno Model | Hopfield NN |

[77] | 10 | ANFIS | 85% | Sugeno Model | – |

[78] | 50 | ANFIS | – | Sugeno Model | – |

[79] | 25 | Neuro Fuzzy | – | Sugeno Model | Variable Step Size |

[80] | 25 | ANFIS-ABC | 98.39% | Sugeno Model | – |

[81] | 2 | Fuzzy Adaptive | 99.21% | Mamdani | RBF-NN |

**Table 3.**Feature review for MPPT controller agorithms using hybrid (HIS) algorithms NN + metaheuristic.

References | Type | Efficiency % | Metaheuristic Algorithm | Network Architecture | Inference System |
---|---|---|---|---|---|

[82] | EANFIS+PSO | 94% | Particle Swarm | – | Sugeno Model |

[83] | FA-ANFIS-P&O | – | Modified Firefly Algorithm | – | Takagi-Sugeno |

[84] | ANFIS–PSO | 97% | Particle Swarm | RLSE | Takagi-Sugeno-Kang |

[85] | Hybrid (Fuzzy+NN+GA) | – | Genetic | MLP | – |

[86] | Hybrid (NN+PSO) | 92.7–99.7% | Particle Swarm | Backpropagation | – |

[87] | Hybrid (NN+SMC) | 96.2% | Secuential Monte-Carlo | MLFF | – |

[88] | Hybrid (ANN+GA) | – | Genetic | MLP | – |

[89] | Hybrid (ANN+PSO) | 99.89% | Particle Swarm | FFBP | – |

[90] | ANFIS–PSO | 98.35% | Particle Swarm | MFNN | max–min Mamdani |

[91] | Hybrid (ANN+GA+BR) | 85% | Genetic | Bayesian Regulation | – |

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**MDPI and ACS Style**

Villegas-Mier, C.G.; Rodriguez-Resendiz, J.; Álvarez-Alvarado, J.M.; Rodriguez-Resendiz, H.; Herrera-Navarro, A.M.; Rodríguez-Abreo, O. Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review. *Micromachines* **2021**, *12*, 1260.
https://doi.org/10.3390/mi12101260

**AMA Style**

Villegas-Mier CG, Rodriguez-Resendiz J, Álvarez-Alvarado JM, Rodriguez-Resendiz H, Herrera-Navarro AM, Rodríguez-Abreo O. Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review. *Micromachines*. 2021; 12(10):1260.
https://doi.org/10.3390/mi12101260

**Chicago/Turabian Style**

Villegas-Mier, César G., Juvenal Rodriguez-Resendiz, José M. Álvarez-Alvarado, Hugo Rodriguez-Resendiz, Ana Marcela Herrera-Navarro, and Omar Rodríguez-Abreo. 2021. "Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review" *Micromachines* 12, no. 10: 1260.
https://doi.org/10.3390/mi12101260