## 3 Theoretical Framework
### MPPT Controller and Motion Detection  
The modeled solar element consists of 10 panels, with 72 cells, each one, and arranged in series to generate a total output power of 2.5 kilowatts. The parameters of this structure are given in Table 1.  
The required area for installing the proposed structure, which includes 720 PV cells, is approximately 37.5 m\({}^{2}\). This area includes the total space required for installation and access for maintenance. For a 10 MW power plant, 40,000 panels (4000 elements) are required, occupying 15 hectares (150,000 m\({}^{2}\)) of total area. The output power curves for this element under different temperatures and irradiation conditions are shown in Figure 7. It is assumed that each panel, comprising 72 solar cells, has its own MPPT. Details of the MPPT system are shown in Figure 8. The proposed MPPT consists of two main MPPT and MPPT subsystem blocks. Figure 6: Block diagram of the proposed ANFIS controller for the studied PV system. The \(Vpv_{E}\) prediction input was added in the proposed approach to the MPPT subsystem method [70; 71; 72] (Figure 8). When the input value of \(Vpv_{E}\) changes, the MPPT algorithm block is bypassed in the first step, and the duty cycle of the controller is updated with the \(Vpv_{E}\) value. The MPPT algorithm block, which is based on INC, as shown in Figure 9, and the MPPT subsystem, shown in Figure 8, were selected based on a renewable energy example of a solar PV system with MPPT using a boost converter. This setup is available in the Simulink environment of the Matlab R2024a software, with some modifications (see [[https://www.mathworks.com/help/sps/ug/solar-pv-system-maximum-power-point-tracking-using-boost-converter.html](https://www.mathworks.com/help/sps/ug/solar-pv-system-maximum-power-point-tracking-using-boost-converter.html)]([https://www.mathworks.com/help/sps/ug/solar-pv-system-maximum-power-point-tracking-using-boost-converter.html](https://www.mathworks.com/help/sps/ug/solar-pv-system-maximum-power-point-tracking-using-boost-converter.html)) (accessed on 20 September 2024)). \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline \(I_{Short\_STC}\) & 8.88 A \\ \(Is_{1}\) & 1 u \\ \(Is_{2}\) & 1 u \\ \(I_{Photon\_STC}\) & 8.88 A \\ \(GSTC\) & 1000 W/m\({}^{2}\) \\ \(N_{1}\) & 1.5 \\ \(N_{2}\) & 1.5 \\ \(R_{SE}\) & 0 \\ \(R_{Parallel}\) & \(\infty\) \\ \(K_{S}\) & 0 \\ \(T_{STC}\) & 25 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the studied PV system. Figure 8: Proposed MPPT controller. Figure 7: Solar element power curve. The proposed approach's main contribution involves adding an extra input (\(Vpv_{E}\)) to the MPPT controller. This input is updated based on the prediction of irradiation changes due to cloud movement across the solar farm. In the current study, the solar farm is modeled as a grid of solar panels (Figure 10), where each panel has a controller. In this figure, the panels are represented by white dots and the remaining area is in black. The grid is assumed to be perfectly regular, with the panel spacing being completely symmetrical. Figure 10b shows the cloud cover entering the solar farm area on a sunny day, which disrupts the irradiation on some of the panels. Assuming vertical solar radiation, the shadow position on the ground will be as shown in Figure 10c. The proposed approach predicts the movement of the shadow on the panels and, consequently, predicts the changes in the MPPT controller, thus increasing the MPPT speed. The most important advantage of the proposed approach is that it does not require a radiation sensor, as it can detect changes in radiation on the panels based on changes in the PV cell's output. The proposed method can determine changes in radiation by using the output of the solar cells, which directly correlates with the sun's radiation. As shown in Figure 10c, the output of cells in the shaded area changes.