International Journal of Advanced Engineering Application

Particle Swarm Optimisation-Based Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions

Author(s):Kavitha M., Anil Kumar S.

Affiliation: Department of Electrical Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India School of Electrical, Electronics and Communication Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

Page No: 28-35

Volume issue & Publishing Year: Volume 3, Issue 6, 2026/06/06

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

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Abstract:
Photovoltaic (PV) systems operating under partial shading conditions (PSC) — arising from clouds, trees, inter-row shading, soiling, and passing obstructions in rooftop and utility-scale installations — exhibit multiple local maximum power points (LMPP) on their power–voltage (P–V) characteristic curves, rendering conventional gradient-based Maximum Power Point Tracking (MPPT) algorithms such as Perturb-and-Observe (P&O) and Incremental Conductance (INC) susceptible to entrapment at LMPPs and consequent substantial power loss. This paper proposes a Particle Swarm Optimisation-based MPPT (PSO-MPPT) controller for a 2 kW grid-connected PV system, designed and validated in MATLAB/Simulink R2023b. The PSO-MPPT controller is benchmarked against P&O, INC, and a Fuzzy Logic MPPT (FL-MPPT) controller under five test scenarios: Standard Test Conditions (STC); uniform irradiance variation (200–1000 W/m² step sweep); PSC Pattern 1 (two-module string, 1000/600 W/m²); PSC Pattern 2 (three-module string, 1000/700/400 W/m²); and dynamic random irradiance (real measured irradiance profile, Coimbatore, June 2024). Evaluation metrics include tracking efficiency (η MPPT), settling time (T₅), steady-state power oscillation (σP), and total energy harvested per day. PSO-MPPT achieves η MPPT = 98.9% at STC, outperforming P&O (92.3%), INC (93.8%), and FL-MPPT (97.4%). Under PSC Pattern 2, PSO-MPPT extracts 97.1% of theoretical maximum power versus 71.4% for P&O (which converges to LMPP) — a 35.9% energy recovery improvement. Dynamic irradiance simulation yields a 9.7% increase in daily energy harvest for PSO-MPPT versus P&O. Hardware-in-the-Loop (HIL) validation using a dSPACE DS1104 controller board confirms simulation results within ±1.8% deviation. The proposed PSO-MPPT algorithm’s computational efficiency, LMPP-avoidance capability, and parameter-free implementation make it directly suitable for microcontroller deployment in commercial grid-tied and off-grid PV inverters.

Keywords: photovoltaic, MPPT, partial shading, particle swarm optimisation, P&O, incremental conductance, fuzzy logic, MATLAB/Simulink, grid-connected PV, hardware-in-the-loop

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