Performance Evaluation of a Hybrid PSO-CSA Based MPPT Controller for Grid-Connected and Standalone Solar PV Systems under Dynamic Irradiance Conditions

Author

Arti Bansal, Happy Dabla, Gaurav Gangil

Abstract

 Solar photovoltaic (SPV) installations are deployed in both grid-connected and standalone configurations, and in each case the Maximum Power Point Tracking (MPPT) controller must remain robust to partial shading, temperature drift and irradiance fluctuations that are typical of field operation. This paper examines the system-level integration and field performance of a hybrid Particle Swarm Optimization-Cuckoo Search Algorithm (PSO-CSA) MPPT controller within representative grid-connected and standalone SPV architectures. The PV array, power-conditioning interface and inverter/battery paths for each configuration are modeled, and the hybrid PSO-CSA controller is benchmarked in MATLAB/Simulink against Perturb and Observe (P&O), Incremental Conductance (IncCond), standalone PSO, standalone CSA and an Adaptive Neuro-Fuzzy Inference System (ANFIS) tracker, with emphasis on robustness to environmental variability and on energy yield over a simulated 8-hour operating window. The hybrid PSO-CSA controller achieved the highest qualitative robustness rating across partial shading, temperature and irradiance-fluctuation scenarios, and delivered an energy yield of 97.5 Wh at 98.0% efficiency, compared with 85.2-86.0 Wh (88.5-89.0% efficiency) for the conventional P&O and IncCond controllers. The results demonstrate that the proposed hybrid MPPT strategy is well suited for practical deployment in both utility-interactive and off-grid SPV systems where environmental conditions are variable and unpredictable.

Keywords

Grid-Connected PV System; Standalone PV System; Maximum Power Point Tracking (MPPT); Hybrid PSO-CSA; Partial Shading; Energy Yield; Robustness; Power Conditioning Unit.

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References

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