GMPPT Algorithm Based Maximum Power Tracking under Dynamic Weather Conditions Employing Krill-Herd Technique

As solar energy extraction becomes more and more popular, attempts are made to further improve the efficiency and reliability of such systems. At the time of partially shaded conditions, the peak power that can be extracted from the PV modules may be more, but the conventional control algorithms may result in the underutilization of the panel. The Maximum Power Point Tracking (MPPT) algorithms like Perturb and Observance (P&O), Incremental Conductance, and other conventional methods fail to detect the global maximum power point (GMPP). In such conditions, a GMPP algorithm is recommended. This paper proposes a new GMPP algorithm called Krill-Herd (K-H) to augment the performance of PV modules with non-uniform irradiations. Krill is a marine animal, and researchers have shown keen interest in this herd due to its ability to form large swarms. The hardware experimentation with a highly efficient Interleaved Boost Converter (IBC) with GaN devices under different partially shaded conditions is used to validate the proposed algorithm. IBC has many advantages over conventional boost converters such as low input current ripple, high efficiency, fast transient response, and improved reliability, less current stress on switching devices, and reduction in filter size. The K-H algorithm shows improved performance over the conventional P&O based algorithms with no oscillations around peak power point, less time to reach steady-state, less power loss, more accuracy, and fewer numbers of iterations. Five different conditions of operation of the panels in real time applications have been considered and is demonstrated that higher efficiency levels of around 99% could be obtained using the K-H algorithm in all these cases.