Enhanced Efficiency in Dual-Stage Grid-Connected Photovoltaic Systems with PSO-Integrated Hybrid MPPT Techniques
DOI:
https://doi.org/10.47852/bonviewAAES62027729Keywords:
particle swarm optimization, maximum power point tracking, incremental conductance, perturb and observe, grid-connected photovoltaic systemAbstract
Maximum power point tracking (MPPT) is essential for maximizing photovoltaic (PV) energy conversion under variable irradiance and temperature. Conventional perturb and observe (P&O) and incremental conductance (INC) algorithms are simple to implement but may exhibit slow convergence, steady-state oscillations, and reduced tracking accuracy during rapid environmental changes. This study proposes two hybrid MPPT controllers—particle swarm optimization (PSO)-assisted P&O and PSO-assisted INC for a dual-stage grid-connected PV system. PSO adaptively determines the perturbation step size, while bounded duty-cycle constraints, transient PSO execution, and reduced perturbation near the maximum power point are applied to limit control jitter and improve stability. The proposed controllers were evaluated in MATLAB/Simulink under time-varying irradiance and temperature and compared with conventional P&O and INC methods. Both hybrid approaches improved power tracking, reduced oscillations at the DC–DC boost converter output, and maintained stable grid synchronization. The PSO–P&O controller achieved 97% efficiency, whereas PSO–INC reached 98.5%, demonstrating superior tracking accuracy and steady-state performance. These findings indicate that combining global PSO search with deterministic local tracking can enhance the efficiency, robustness, and reliability of grid-connected PV systems. Future work should validate the controllers experimentally under partial shading, grid disturbances, and real-time hardware conditions.
Received: 22 September 2025 | Revised: 22 June 2026 | Accepted: 4 August 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data are available from the corresponding author upon reasonable request.
Author Contribution Statement
Yam Krishna Poudel: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Jeewan Phuyal: Formal analysis, Investigation, Data curation, Writing - review & editing, Visualization, Project administration. Asmita Rijal: Investigation, Resources. Rashik Kumar Badgami: Software, Resources.
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