Abstract
One of the major challenges in Photovoltaic (PV) systems is the effective tracking of the Maximum
Power Point (MPP) during Partial Shading Conditions (PSC). Partial shading occurs when only a portion
of the PV array is shaded due to obstacles like trees, buildings, or clouds. One possible result of long term partial
shading is the hotspot phenomenon, where the undesirable temperature increase causes
permanent damage to the solar cells. The utilization of bypass diodes is a common solution to prevent
hotspot problems and associated power loss. However, this results in multiple local maxima in the
power voltage curve, making it difficult for traditional Maximum Power Point Tracking (MPPT)
algorithms to accurately locate the Global Maximum Power Point (GMPP). Addressing this issue is
crucial for enhancing the overall energy conversion efficiency and reliability of PV systems. Utilizing
novel approaches to improve energy conversion efficiency is crucial in photovoltaic systems and
subsequently lower the costs of PV energy. An efficient MPPT controller which can track the optimum
operating point of the DC-DC converter which in turn ensure maximum power is transferred from the
source to the load during PSC. The research explored the feasibility of employing AI driven strategies to
address the limitations of traditional MPPT methods controllers during PSC in PV systems. The novelty
of this research is in applying an actor-critic based deep reinforcement learning agent, specifically the
Soft Actor Critic (SAC), to detect the global maximum during partial shading. The SAC reinforcement
learning agent replaces the traditional controller and acquires knowledge through its interactions
within the environment. The proposed Deep Reinforcement Learning (DRL) based approach offers
advantages, including the elimination of the need for real world training data. The SAC agent was
designed and trained using MATLAB and Reinforcement Learning (RL) Toolbox. The proposed MPPT
controller has been designed and tested through simulations in MATLAB Simulink. The proposed DRL
MPPT method enhances tracking efficiency to 99.9%, achieving rapid GMP tracking with an average
tracking time of 0.304 seconds and a settling time of 0.14 seconds. The MPPT controller tracked the
GMP with minimal oscillations around the Maximum Power Point (MPP), resulting in a steady state
efficiency of 97%. The results demonstrate superior GMP tracking under both static and dynamic PSC
scenarios. The SAC MPPT shows a rapid tracking response and minimal oscillations, enhancing system
stability. The SAC MPPT controller improves GMP tracking efficiency compared to traditional and other
AI-based MPPT methods. Furthermore, the results are validated using Hardware in the Loop (HIL)
testing, showing good agreement with the simulation outcomes.