# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods
### Reinforcement Learning Fuzzy-DDPG
## 9 Conclusions

In this work, we proposed a novel hybrid reinforcement learning approach that integrates fuzzy strategies with the Deep Deterministic Policy Gradient (DDPG) algorithm for Maximum Power Point Tracking (MPPT) in photovoltaic systems under partial shading conditions. The resulting Fuzzy-DDPG algorithm is specifically designed to address the challenges posed by the power-voltage characteristics of partially shaded PV arrays, where traditional methods often converge to suboptimal local maxima. The experimental results demonstrate that the Fuzzy-DDPG method achieves superior performance in terms of global optimality, convergence speed, tracking accuracy, and control stability when compared with conventional techniques such as Perturb and Observe (P&O) and Incremental Conductance (IC), as well as with the standard DDPG algorithm. The incorporation of fuzzy logic into the reinforcement learning framework effectively enhances the exploration-exploitation balance, allowing states to be mapped onto smooth functions. The algorithm aims to reliably identify and track the global maximum power point under a variety of environmental conditions. Theoretical analysis further corroborates the convergence properties of the hybrid approach, while statistical evaluations confirm its robustness and consistency in power extraction performance across diverse irradiance and temperature scenarios. These findings underscore the potential of integrating fuzzy approximation techniques with deep reinforcement learning to overcome the nonlinearities and uncertainties in photovoltaic systems. Future research will focus on adaptive fuzzy rule tuning, scalability studies for large-scale PV installations to further enhance the applicability, and the search for reduced hardware costs so that its implementation is more accessible to society. Conceptualization, D.L.-C. and D.O.-M.; methodology, D.L.-C.; software, D.O.-M.; validation, L.A.P.-D., A.G.R.-R. and F.G.-L.; writing--original draft preparation, D.O.-M. All authors have read and agreed to the published version of the manuscript. This research was funded by Mexico SECIHTI grant number 1144762. ## References

* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] [PERSON] JAYA Algorithm Based on Levy Flight for Global MPPT Under Partial Shading in Photovoltaic System. _Inst. Electr. Electron. Eng._**2020**, \(9\), 4979-4991. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] MPPT Design for a DC Stand-Alone Solar Power System with Partial Shaded PV Modules. In Proceedings of the 2019 International Conference on System Science and Engineering (ICSSE), Dong Hoi, Vietnam, 20-21 July 2019; Volume 32, pp. 31-36. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System. _Inst. Electr. Electron. Eng._**2020**, \(9\), 48679-48703. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] Global MPPT based on flower pollination and differential evolution algorithms to mitigate partial shading in building integrated PV system. _Sol. Energy_**2017**, _157_, 171-186. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions. _Renew. Sustain. Energy Rev._**2018**, _92_, 513-553. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] Maximum power point tracking under partial shading condition using particle swarm optimization with DC-DC boost converter. In Proceedings of the 2018 53 rd International Universities Power Engineering Conference (UPEC), Glasgow, UK, 4-7 September 2018; pp. 1-6. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON] [PERSON] A Novel Hybrid Whale Optimization Algorithm Differential Evolution Algorithm-Based Maximum Power Point Tracking Employed Wind Energy Conversion Systems for Water Pumping Applications: Practical Realization. _IEEE Trans. Ind. Electron._**2024**, _71_, 1641-1652.