Application of DSO algorithm for estimating the parameters of triple diode model-based solar PV system

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Computational Intelligence

Background:

• Solar Photovoltaic (SPV) technology is crucial for decarbonizing the energy grid and improving energy efficiency.
• Accurate modeling of SPV systems is challenging due to inherent nonlinearities.
• Precisely identifying parameters in lumped electric circuit models is essential for reliable SPV performance prediction.

Purpose of the Study:

• To develop a novel, accurate method for estimating the 15 parameters of a three-diode equivalent circuit model for solar PV systems.
• To address the nonlinear modeling challenges in SPV systems.
• To improve the simulation of P-V and I-V performance curves for enhanced accuracy.

Main Methods:

• A new optimization technique, Drone Squadron Optimization (DSO), inspired by human artifacts, is proposed.
• The DSO method is applied to estimate parameters for a three-diode equivalent circuit model of SPV systems.
• The technique's performance is evaluated using two commercial PV cells (RTC France and photo watt-201) under various climatic conditions.

Main Results:

• The DSO technique accurately simulates the nonlinear relationship between P-V and I-V curves, minimizing the discrepancy between experimental and calculated data.
• The proposed method yields highly accurate parameter estimates for SPV systems.
• The DSO technique achieved the lowest Root Mean Square Error (RMSE) for both RTC (6.7776 × 10^-4) and PWP Photo Watt (0.002310324 × 10^-4) cells compared to existing methods.

Conclusions:

• Drone Squadron Optimization offers a superior approach for parameter estimation in complex solar PV models.
• The method enhances the accuracy of SPV system modeling, contributing to more reliable energy grid integration.
• DSO demonstrates robust adaptive performance across different climatic states, validating its effectiveness for real-world applications.