Research on photovoltaic MPPT under complex conditions based on an advanced great wall construction algorithm method

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Optimization Algorithms

Background:

• Photovoltaic (PV) systems exhibit complex non-linear, multi-peak power-voltage characteristics under partial shading.
• Conventional Maximum Power Point Tracking (MPPT) algorithms struggle to achieve optimal performance in these challenging conditions.
• Suboptimal MPPT leads to significant energy losses and reduced overall system efficiency.

Purpose of the Study:

• To develop a novel MPPT algorithm that overcomes the limitations of existing methods under partial shading.
• To enhance the tracking speed and accuracy of MPPT for photovoltaic systems.
• To minimize power losses and maximize energy conversion efficiency in PV systems.

Main Methods:

• Introduction of the LGWGCA-P&O method, a hybrid approach combining a modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique.
• The LGWGCA incorporates a Grey Wolf Optimization (GWO)-inspired positional update mechanism and a Levy flight strategy for improved agent distribution and accelerated tracking.
• The algorithm seamlessly transitions to the P&O method near the maximum power point for precise identification.

Main Results:

• The LGWGCA-P&O method achieved a PV conversion efficiency exceeding 99.96%, significantly reducing power losses.
• Convergence speed was enhanced by approximately 40% compared to traditional GWO-P&O methods.
• The proposed method demonstrated superior performance, computational efficiency, and robustness against Particle Swarm Optimization and Cuckoo Search Optimization under extreme conditions.

Conclusions:

• The developed LGWGCA-P&O method effectively addresses the challenges of MPPT under partial shading conditions in PV systems.
• This hybrid approach offers a significant improvement in tracking speed, accuracy, and overall energy conversion efficiency.
• The LGWGCA-P&O method presents a robust and computationally efficient solution for optimizing PV system performance.