# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods
### Reinforcement Learning Fuzzy-DDPG
The experiments were carried out in Ciudad Juarez, Mexico, where the annual solar irradiance benefits from over 300 sunny days, ensuring a robust environment for photovoltaic (PV) energy harvesting. The experimental setup comprises two PV panels in series operating under partial shading conditions, interfaced with a DC-DC converter whose duty cycle is modulated by the Fuzzy-DDPG controller. Figure 13: Convergence time comparison between DDPG and Fuzzy–DDPG. ### Experimental Setup

The experimental platform integrates measurement instruments, a microcontroller-based DC-DC converter, and a hardware-in-the-loop configuration for real-time execution of the Fuzzy-DDPG algorithm. Specifically, the photovoltaic panels (electrical specifications are summarized in Table 8) serve as the primary energy source. Precision sensors for voltage and current measurement ensure accurate data acquisition, while a dedicated PC and an Android-based interface provide robust system control and monitoring. This integrated setup enables the capture of dynamic system behavior under partial shading conditions, thereby bridging the gap between simulation and practical application. The following sections detail the experimental components used in the validation of the Fuzzy-DDPG algorithm with 2 PV panels connected in series of 25 watts of power each. ### MPPT Circuit Design

Figure 14 illustrates the schematic diagram of the hardware implementation used for the proposed MPPT controller. The diagram highlights the main functional blocks, including the DC-DC converter stage, the current and voltage sensing modules, and the microcontroller responsible for running the hybrid Fuzzy-DDPG algorithm. The input from the photovoltaic panel is processed by the sensor circuits, which feed the measured signals into the microcontroller for real-time duty-cycle adjustments. This schematic provides an overview of how each component interacts to ensure accurate power tracking and efficient energy transfer under varying irradiation and temperature conditions.The detailed specifications of these components are provided in Table 9.

The converter is controlled by a PC that executes the Fuzzy-DDPG algorithm in real time. The integration of fuzzy logic with the DDPG framework enhances the state-space exploration and accelerates convergence toward the optimal operating point. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of Panels & 2 \\ Maximum Power (P\({}_{\text{max}}\)) & 25 W \\ Open-Circuit Voltage (Voc) & 21.6 V \\ Short-Circuit Current (Isc) & 1.58 A \\ Voltage at MPP (Vmp) & 17.4 V \\ Current at MPP (lmp) & 1.44 A \\ Dimensions & 550 \(\times\) 330 mm \\ Shunt Resistance (R\({}_{\text{sh}}\)) & 10637 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Photovoltaic panel specifications. \begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Function** \\ \hline Solar Panel SE-156*2 (25 W) & Energy source (25 W) \\ CN3795 (MPPT Controller) & Regulates maximum power point for battery charging \\ Battery & Energy storage \\ B3100 BE-13 (Schottky Diode) & Prevents reverse current flow \\ DMP6032S1-13 (MOSFETs) & Filtering in the MPPT stage \\ Inductor (68 uH/2 A) & Filtering and energy storage \\ INA219 (Sensor) & Current and voltage monitoring \\ Raspberry Pi 4 & Data acquisition and local visualization \\ TFT ILI9341 Display & Graphical interface (SPI) \\ WiFi Module & Wireless communication \\ Resistors and Capacitors & Signal conditioning and filtering \\ Apogee SP-510 & Radiation Sensor \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MPPT circuit components. ### System Monitoring and Data Visualization

Figure 15 shows the enclosure housing the MPPT hardware, including the controller board, power electronics, and protective circuitry. The PC laptop running matlab simulink on the right is used for real-time data processing and parameter tuning in a hardware-in-the-loop manner.