Modified Dhole-inspired optimization for maximum power extraction in photovoltaic systems under partial shading.

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Optimization Algorithms

Background:

• Partial shading conditions (PSC) in photovoltaic (PV) systems reduce energy output.
• Extracting maximum power under PSC is challenging for standard Maximum Power Point Tracking (MPPT) algorithms.
• Optimization-based MPPT techniques offer improved performance under complex conditions.

Purpose of the Study:

• To propose a novel Modified Dolphin Hunting Optimization (M-DHO) algorithm for enhanced MPPT under PSC.
• To improve global search capability and avoid local minima using Levy flight integration.
• To evaluate the performance of M-DHO against existing algorithms in diverse PSC scenarios.

Main Methods:

• Developed the M-DHO algorithm by combining the Dolphin Hunting Optimization (DHO) algorithm with a Levy flight strategy.
• Created nine distinct PSC scenarios across six voltage regions for rigorous testing.
• Compared M-DHO performance against Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Firefly Algorithm (FPA), and the conventional DHO algorithm.

Main Results:

• The M-DHO algorithm demonstrated faster convergence to the global maximum power point (GMPP).
• M-DHO achieved higher tracking efficiency compared to benchmark algorithms across all tested scenarios.
• On average, M-DHO achieved 838.58W extracted power with a 0.15s tracking speed and 99.52% efficiency.

Conclusions:

• The proposed M-DHO algorithm is highly effective for MPPT in PV systems experiencing partial shading.
• Integrating Levy flight significantly enhances the global search capability and accuracy of the DHO algorithm.
• M-DHO offers a superior solution for maximizing energy yield from PV systems under challenging irradiance conditions.