Abstract
Recent advancements in electric vehicles (EVs) and modern power systems offer broad opportunities for integrating renewable energy solutions. Solar photovoltaic (PV) systems, in particular, inherently avoid harmonic injection at the source due to the absence of alternating current (AC) power. However, consistently extracting maximum power from PV panels remains a technical challenge—especially under partial shading conditions where conventional algorithms struggle to locate the global maximum on the P–V curve. The recently introduced Jaya optimization algorithm has demonstrated improved performance through its reduced control variables and lower computational demand. Despite these advantages, its random nature often results in wide output fluctuations during transient periods, leading to limited exploitation near the global maximum. To overcome these drawbacks, this article introduces an enhanced Jaya algorithm designed to improve exploitation efficiency while tracking the global maximum power point (MPPT). A Luo DC–DC converter is employed due to its low output ripple, making it suitable for stable power conversion. Extensive simulations and experimental tests were conducted using 4S and 6S PV array configurations rated at 240 W and 360 W, respectively. The proposed method was benchmarked against seven other contemporary optimization algorithms and proved superior—achieving MPPT within 0.1 s and maintaining efficiency above 99% under all shading conditions. Further validation through statistical indices such as IAE, ITAE, ISE, and ITSE confirms the proposed approach’s robustness and suitability for real-time, fast renewable energy applications.
