Frequency Domain-DQN for Flexible Photovoltaic Power Point Tracking Under Noisy Operating Condition

Due to the variability of environmental parameters, Photovoltaic (PV) systems frequently operate under Noisy Operating Conditions (NOC). The existing Flexible Power Point Tracking (FPPT) algorithms encounter significant challenge in misjudging the optimal voltage points under NOC. This paper presents a Frequency Domain-Deep Q-Network (FD-DQN) approach to FPPT by incorporating frequency domain analysis through the Fourier transform, which decomposes irradiance signals to capture dynamic changes caused by noise. Additionally, a novel Signal Quality Factor (SQF) is introduced to quantify noise and refine the learning process, minimizing overfitting in the presence of noisy data. By analyzing the influence of different frequency components, the FD-DQN enables more accurate tracking with fewer misjudgments. Experimental results indicate that the proposed approach achieves a minimum improvement of 5% in tracking accuracy and reduces misjudgments by around 50% under NOC, outperforming traditional methods in both simulated and experimental scenarios with fluctuating irradiance.