Reconfigured single- and double-diode models for improved modelling of solar cells/modules

Area of Science:

• Renewable Energy Engineering
• Photovoltaic Device Modeling
• Computational Intelligence

Background:

• Accurate modeling of photovoltaic (PV) cells and modules is crucial for optimizing PV system efficiency.
• Existing research primarily focuses on the single-diode model (SDM) and double-diode model (DDM), often using metaheuristic algorithms for parameter extraction.
• Limitations in classical models necessitate more representative approaches for precise PV performance prediction.

Purpose of the Study:

• To introduce novel reconfigured single-diode (Reconfig-SDM) and double-diode (Reconfig-DDM) models for enhanced PV device modeling.
• To employ the squirrel search algorithm (SSA) for global parameter identification of the proposed models.
• To validate the effectiveness of the Reconfig-SDM and Reconfig-DDM against experimental data and compare with existing methods.

Main Methods:

• Development of Reconfig-SDM and Reconfig-DDM by incorporating a series resistance into the diodes of classical SDM and DDM.
• Application of the squirrel search algorithm (SSA) for optimizing the parameters of the newly proposed PV models.
• Experimental validation using a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module.

Main Results:

• The proposed Reconfig-SDM and Reconfig-DDM, optimized via SSA, exhibit enhanced accuracy and effectiveness in modeling PV devices.
• Significant error reduction was achieved: up to 0.37% for the solar cell and 3.21% for the solar module compared to the best literature approach.
• Reconfig-DDM showed a remarkable error reduction of 2.58% for the solar cell and 29.0% for the solar module.

Conclusions:

• The novel Reconfig-SDM and Reconfig-DDM provide more accurate and representative modeling of PV devices compared to conventional models.
• The squirrel search algorithm (SSA) is an effective tool for global parameter extraction in these advanced PV models.
• The proposed modeling approach offers a significant improvement in PV system efficiency and performance prediction.