# Neural Network Approach to MPPT Control and Irradiance Estimation
## 5 Conclusions  
In this paper, the simple and accurate gradient MPPT algorithm based on the neural network model of the photovoltaic module has been proposed. The NN model is utilized to calculate the gradient of the _P-V_ curve with respect to the voltage, which enables fast tracking of the MPP and steady-state performance without oscillations. Additionally, the irradiance estimator, which also relies on the NN model, has been presented. It is important to emphasize that the NN model can be developed either by using the experimental data or the data generated by the equivalent circuit model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(G\) & \(P_{max}\) & \multicolumn{5}{c}{**Prediction Error (\%)**} \\ \hline  & & **NMPPT\({}_{e}\)** & **NMPPT\({}_{s}\)** & **EMPPT** & **P\&O** & **CNNMPT** \\ \hline
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 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative error between the predicted and true maximal power.  
Figure 14: Comparison of the algorithms under time varying profile of the solar irradiance.  
Suitable simulations have been performed for comparing our approach with the effective techniques proposed in the literature. The final results indicate that the proposed MPPT algorithm exhibits faster convergence speed compared to the other iterative algorithms. It has been also demonstrated that the experimental-data-based NN model achieves the best steady-state performance, as well as that the acceptable performance can be obtained with the simulated-data-based NN model. Furthermore, the proposed irradiance estimator exhibits greater or similar accuracy compared to the considered algorithms. This is also of great importance since the irradiance estimators can be used for other purposes such as monitoring and fault detection of PV systems. Lastly, the proposed approach does not only achieve high degrees of accuracy, but it also has low computation complexity, which makes it suitable for implementation on microprocessor platforms.  
Conceptualization, Z.Z. and M.R.; methodology, Z.Z. and M.R.; writing--original draft preparation, Z.Z. and M.R.; writing--review and editing, Z.Z. and M.R.; visualization, Z.Z. and M.R. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Authors declare no conflict of interest.