A new optimal control methodology for improving MPPT based on FOINC integrated with FPI controller using AHA

Conventional maximum power point tracking (MPPT) techniques have remarkable oscillation and drawbacks in tracking the maximum power point (MPP) under rapid climate changes. Therefore, this paper proposes a robust MPPT controller based on the fractional-order incremental conductance method (FOINC) and fractional order proportional integral controller (FPI) to catch the MPP of the PV system with a high steady state under different levels of radiation. Integrating fractional-order properties with the MPP controller adds an extra degree of freedom for the system, ensuring fast dynamics and high tracking accuracy. An artificial hummingbird algorithm (AHA) is employed to adaptively tune the FPI's parameters for reliable and self-adapting control. To justify the efficiency of the proposed approach, it is examined with five series of experiments and compared with a set of integer and fractional-based proportional-integral-derivative controllers; moreover, it is validated versus a bunch of state-of-the-art algorithms, including coot bird natural life model (Coot) and chimp optimizer. The comparison reveals that the proposed FOINC integrated with FPI based on AHA can track MPP in 0.1 sec with less oscillation compared with the other approaches that consume more than 0.5 sec; moreover, it provides high levels of harvest power.

Automated summary

This paper proposes a robust MPPT controller based on the fractional-order incremental conductance method and fractional-order proportional integral controller to achieve high steady-state maximum power point tracking in solar PV systems. The controller integrates fractional-order properties for fast dynamics and high tracking accuracy.