Transient Differentiation Maximum Power Point Tracker (Td-MPPT) for Optimized Tracking under Very Fast-Changing Irradiance: A Theoretical Approach for Mobile PV Applications

This work presents an algorithm for Maximum Power Point Tracking (MPPT) that measures transitory states to prevent drift issues and that can reduce steady-state oscillations. The traditional MPPT algorithms can become confused under very fast-changing irradiance and perform tracking in the wrong direction. Errors occur because these algorithms operate under the assumption that power changes in the system are triggered exclusively due to perturbations introduced by them. However, the power increase triggered by irradiance changes could be more significant than those caused by the perturbation effect. The proposed method modifies the Perturb and Observe algorithm (P&O) with an additional measurement stage performed close to the maximum overshoot peak after the perturbation stage. By comparing power changes between three measurement points, the algorithm can accurately identify whether the perturbation was made in the correct direction or not. Furthermore, the algorithm can use additional information to determine if the operating point after the perturbation stage is beyond the maximum power point (MPP) and perturb in the opposite direction for the next iteration. Thus, the proposed algorithm shows reduced steady-state oscillations and improved tracking under fast irradiance changes compared to conventional P&O and P&O with power differences (dP-P&O). The design is validated via simulations using fast-changing irradiance tests based on the standard EN 50530 accelerated by a factor of 100x. The proposed algorithm achieved 99.74% of global efficiency versus 97.4% of the classical P&O and 99.54% of the dP-P&O under the tested conditions.

Automated summary

The paper proposes an algorithm for MPPT that accurately identifies power changes caused by irradiance changes and achieves maximum power point tracking through accurate judgment of the perturbation direction, resulting in better tracking performance compared to traditional algorithms.