# Neural Network Approach to MPPT Control and Irradiance Estimation

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Theoretical Background

#### 2.1. Equivalent Electrical Circuit of PV Module

#### 2.2. Neural Network Model of PV Module

#### 2.3. Overview of MPTT Algorithms

## 3. Proposed MPPT Algorithm and Irradiance Estimator

#### 3.1. NMPPT Algorithm

#### 3.2. Estimation of the Irradiance

#### 3.3. Computational Complexity

Algorithm 1 The Proposed Algorthm. |

Initialization:$V\left(0\right)$, $\widehat{G}\left(0\right)$for each time instantk- 1:
- Measure ${I}_{k},{V}_{k,1},{T}_{k}$
- 2:
**for**$i=1,\dots ,P$**do**- 3:
**u**$={\left[\begin{array}{ccc}{\widehat{G}}_{k,i}& {T}_{k}& {V}_{k,i}\end{array}\right]}^{T}$- 4:
- ${\widehat{I}}_{k,i}=net({\widehat{G}}_{k,i},{T}_{k},{V}_{k,i})$
- 5:
- $\frac{\partial {\widehat{P}}_{k,i}}{\partial {V}_{k}}={\widehat{I}}_{k,i}+{V}_{k,i}\left({\mathbf{W}}_{1,3}^{T}\odot {\mathbf{W}}_{\mathbf{2}}\right){tanh}^{\prime}\left({\mathbf{W}}_{1}\mathbf{u}+{\mathbf{b}}_{1}\right)$
- 6:
- ${V}_{k,i+1}={V}_{k,i}+\mu \frac{\partial {\widehat{P}}_{k,i}}{\partial {V}_{k}}$
- 7:
- ${e}_{k,i}={I}_{k}-{\widehat{I}}_{k,i}$
- 8:
- ${\widehat{G}}_{k,i+1}={\widehat{G}}_{k,i}+\gamma {e}_{k,i}$
- 9:
**end for**- 10:
- ${\widehat{G}}_{k+1,1}={\widehat{G}}_{k,P}$
end for |

## 4. Simulation Results

#### 4.1. Simulated Data

#### 4.2. Experimental Data

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 6.**Comparison between P-V curves of PV module generated by the equivalent circuit and NN model of the photovoltaic.

k | G | ${\mathit{P}}_{\mathit{max}}$ | Prediction Error (%) | |||
---|---|---|---|---|---|---|

NMPPT | P&O | EMPPT | CNNMPT | |||

20 | 140 | 59.429 | 18.881 | 32.397 | 56.772 | 0.251 |

40 | 240 | 103.593 | 0.004 | 0.011 | 46.72 | 0.003 |

60 | 340 | 147.954 | 0.002 | 0.089 | 31.677 | 0.084 |

80 | 440 | 192.26 | 0.001 | 0.148 | 18.353 | 0.148 |

100 | 540 | 236.382 | 0.0001 | 0.224 | 6.007 | 0.171 |

120 | 640 | 280.24 | 0.001 | 0.339 | 0.366 | 0.159 |

140 | 740 | 323.775 | 0.0001 | 0.485 | 2.118 | 0.124 |

160 | 840 | 366.95 | 0.001 | 0.668 | 1.509 | 0.079 |

180 | 940 | 409.733 | 0.0001 | 0.885 | 1.708 | 0.036 |

200 | 1040 | 452.106 | 0.001 | 1.138 | 1.004 | 0.008 |

G | ${\mathit{P}}_{\mathit{max}}$ | Prediction Error (%) | ||||
---|---|---|---|---|---|---|

NMPPT${}_{\mathit{e}}$ | NMPPT${}_{\mathit{s}}$ | EMPPT | P&O | CNNMPT | ||

110.2 | 22.77 | 0.06 | 0.082 | 0.408 | 1.856 | 1.834 |

132.2 | 23.635 | 0.099 | 0.064 | 0.218 | 0.187 | 0.712 |

225.1 | 46.391 | 0.003 | 0.012 | 0.097 | 0.11 | 0.177 |

260.5 | 54.285 | 0.007 | 0.007 | 0.058 | 0.321 | 0.536 |

302.9 | 61.845 | 0.004 | 0.012 | 0.046 | 0.117 | 0.537 |

369.9 | 77.121 | 0.01 | 0.002 | 0.023 | 0.291 | 0.333 |

409.7 | 84.877 | 0.001 | 0.001 | 0.005 | 0.24 | 0.43 |

523.5 | 111.36 | 0.009 | 0.051 | 0.064 | 0.6328 | 0.016 |

613.3 | 120.578 | 0.006 | 0.267 | 0.023 | 0.027 | 0.04 |

653.8 | 133.64 | 0.007 | 0.01 | 0.016 | 1.746 | 0.046 |

713.40 | 153.97 | 0.0004 | 0.2259 | 0.2473 | 0.2680 | 0.9709 |

859.5 | 171.037 | 0.0001 | 0.005 | 0.002 | 0.006 | 0.262 |

946.8 | 198.125 | 0.001 | 0.142 | 0.163 | 0.72 | 0.572 |

1007.1 | 208.772 | 0.004 | 0.06 | 0.07 | 0.123 | 1.581 |

1084.3 | 228.473 | 0.003 | 0.173 | 0.118 | 0.209 | 2.586 |

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**MDPI and ACS Style**

Zečević, Ž.; Rolevski, M. Neural Network Approach to MPPT Control and Irradiance Estimation. *Appl. Sci.* **2020**, *10*, 5051.
https://doi.org/10.3390/app10155051

**AMA Style**

Zečević Ž, Rolevski M. Neural Network Approach to MPPT Control and Irradiance Estimation. *Applied Sciences*. 2020; 10(15):5051.
https://doi.org/10.3390/app10155051

**Chicago/Turabian Style**

Zečević, Žarko, and Maja Rolevski. 2020. "Neural Network Approach to MPPT Control and Irradiance Estimation" *Applied Sciences* 10, no. 15: 5051.
https://doi.org/10.3390/app10155051