# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods
### Reinforcement Learning Fuzzy-DDPG
Additionally, the Android-based interface (depicted in the figure) allows remote monitoring of voltage, current, power, and battery status. By displaying these parameters continuously, the user can promptly assess the performance of the Fuzzy-DDPG algorithm under varying irradiance conditions. Figure 15 thus provides a holistic view of the entire setup, illustrating how the panels, control hardware, and monitoring devices are arranged in practice. ### Experimental Results

Figure 16 shows the evolution of the measured power output throughout the day (from 10:00 h to 18:00 h) for two series-connected photovoltaic panels. The data were collected under actual operating conditions in Ciudad Juarez, Mexico, during a typical January day, during which multiple partial shading events occurred. Under these challenging conditions, the proposed Fuzzy-DDPG algorithm for Maximum Power Point Tracking (MPPT) demonstrated robust performance by rapidly converging to the global maximum power point despite fluctuations in irradiance and shading levels. The integration of fuzzy logic within the Deep Deterministic Policy Gradient framework effectively mitigates the risk of becoming trapped in local maxima while maintaining high tracking accuracy. Furthermore, the fuzzy approximation layer produced smoother

Figure 14: Schematic diagram of the MPPT circuit. control actions, thereby reducing power oscillations when compared to conventional methods such as Perturb and Observe or Incremental Conductance. However, several challenges were encountered during the experimental implementation. A notable issue was the increased memory requirement for inference when using the Fuzzy-DDPG algorithm. The enhanced computational demands of the fuzzy approximation and deep learning components necessitated devices with greater memory capacity compared to simpler algorithms like Perturb and Observe. Additionally, our current implementation employed a hardware-in-the-loop approach--conducting inference on a PC rather than deploying on embedded hardware. This highlights the need for more capable hardware for real-time applications and underscores the trade-off between achieving advanced tracking performance and the limitations imposed by available hardware resources. Figure 16: Experimental power output over time for two series-connected 25 W PV panels under partial shading conditions. Figure 15: Physical implementation of the MPPT system. The image shows the panels under partial shading conditions, along with the power electronics, control hardware, and battery enclosure. Preliminary experimental results confirm that the proposed Fuzzy-DDPG algorithm is capable of accurately tracking the GMP, even in the presence of multiple local maxima induced by partial shading. The controller exhibits rapid convergence and reduced oscillatory behavior compared to conventional methods. These results underscore the effectiveness of the hybrid fuzzy-reinforcement learning strategy for MPPT in PV systems and motivate further statistical analysis and optimization in future work. The statistics presented in Table 10 demonstrate the efficacy of the proposed Fuzzy-DDPG algorithm in accurately identifying the maximum power point under partial shading conditions. The measured mean power output closely approximates the ideal value, with a relative error of only 3.0%. Moreover, the maximum observed power reaches the theoretical limit, thereby confirming that the controller consistently achieves optimal tracking. The low standard deviation further indicates stable operation with minimal fluctuations, underscoring the robustness of the Fuzzy-DDPG approach in dynamic photovoltaic environments. ## 8 Discussion

The experimental results justify the efficacy of the proposed Fuzzy-DDPG algorithm in achieving Maximum Power Point Tracking (MPPT) under partial shading conditions.