Real-Time MPPT using Mantis Shrimp Optimization for PV Systems under Partial Shading

Abstract

Solar energy, in particular, is a renewable energy source that offers an ideal solution for power generation because of its sustainability and eco-friendliness. However, their efficiency is significantly influenced by climatic variations, especially temperature and sun irradiance. The irradiance level is monitored to maximize power generation from solar energy, resulting in the development of various Maximum Power Point Tracking (MPPT) approaches. When Partial Shading (PS) occurs, the characteristics of Power-Voltage (P_V) of PV systems include multiple peaks, making it harder for traditional MPPT-methods to effectively track the Global Maximum Power Point (GMPP). This is where meta-heuristic optimizations come into play to address this challenge. Accordingly, this study proposes a novel optimization-based approach to real time tracking of the maximum-power operating point, grounded in behavioral models inspired by the sensory and decision making capabilities of a marine organism known for its speed and precision in dynamic environments, known as the Mantis Shrimp Optimization Algorithm (MShOA). The primary objective of this research is to advance the development of a behaviorally inspired algorithm, ensure its integration into the control architecture of photovoltaic tracking systems, and enhance overall system performance by improving convergence speed, tracking accuracy, and resilience to environmental variability. To validate this proposed approach, the MShO algorithm is compared with highly efficient MPPT-based techniques such as Flower Pollination Algorithm (FPA), and Grey Wolf Optimization (GWO) in a real-time experimental laboratory setup using a Dspace 1104 board and PV simulator. Results demonstrate that suggested approach exhibits better responsiveness, less power oscillation, and higher tracking efficiency under a variety of operating conditions. The findings have broader implications for the field of renewable energy, suggesting that biologically grounded optimization methods offer a promising pathway toward more intelligent, adaptive, and efficient energy management technologies suitable for modern grid applications and future decentralized energy infrastructures.