.png){kind=icon}
.png){kind=icon}
Return
Enhancing MPPT Performance Using Adaptive Population Size and Run Length Distribution Analysis: A Simulation and Experimental Study
.png){kind=icon}
OA
AI
S
K
S
J
DOI：10.1002/ese3.70345.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents an adaptive population size (NP)–based accelerated Particle Swarm Optimization (AAPSO) algorithm for duty cycle–based maximum power point tracking (MPPT) in photovoltaic (PV) systems. The proposed method directly modulates the duty cycle of a DC–DC converter, enabling rapid and precise adjustments to the maximum power point (MPP) under both uniform and partial shading conditions. AAPSO enhances conventional PSO by adopting a social-only variant and an adaptive Population size (NP) mechanism that begins with a large population for exploration and gradually reduces it to balance exploration and exploitation. To ensure robustness, the algorithm is executed 100 times, and performance is analyzed using statistical metrics and run-length distribution (RLD). Simulation results demonstrate approximately 99.8% tracking efficiency with a 100% tracking accuracy across all runs, while convergence counts are reduced nearly threefold compared to conventional Particle Swarm Optimization (CPSO) and two recent adaptive PSO-based MPPT methods from the literature. Experimental validation using a Ćuk converter prototype further confirms its practical feasibility. Overall, this study contributes an adaptive, duty cycle–based constrained PSO framework that integrates robustness, scalability, and statistical reliability for MPPT in large-scale PV systems.
Keywords:
characteristic curve
DC-DC converter
evolutionary algorithm
maximum power point tracking
partial shading
Particle Swarm optimization
Photovoltaic
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
2.3K
Citations:
5.7K
Organization
Cited Papers