Advanced extraction of PV parameters' models based on electric field impacts on semiconductor conductivity using QIO...

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Computational Optimization

Background:

• Accurate parameter estimation is crucial for modeling photovoltaic (PV) cells and modules.
• Traditional models often overlook the impact of variable voltage resistances (VVR) on semiconductor conductivity.
• Existing optimization algorithms may lack efficiency and robustness for complex PV parameter extraction.

Purpose of the Study:

• To present a novel approach for PV parameter estimation using the Quadratic Interpolation Optimization Algorithm (QIOA).
• To incorporate variable voltage resistances (VVR) into PV models for enhanced accuracy.
• To minimize the root mean square error between measured and modeled I-V data for PV devices.

Main Methods:

• Developed a PV model incorporating series and shunt resistances dependent on voltage (VVR).
• Employed the Quadratic Interpolation Optimization Algorithm (QIOA) to optimize unknown PV model parameters.
• Validated the QIOA against Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA).

Main Results:

• The QIOA successfully optimized eleven parameters for a silicon PV module under normal radiation and twelve parameters for a multi-crystalline silicon (MCS) module under low radiation.
• The proposed QIO method achieved the lowest absolute current error values for both Single-Crystalline Silicon (SCS) and MCS cells.
• QIOA demonstrated superior convergence speed and robustness compared to GWO, PSO, SSA, and SCA.

Conclusions:

• The QIOA is a highly effective and efficient algorithm for accurate PV parameter estimation, especially when considering VVR.
• The proposed method provides a more accurate representation of PV cell behavior under varying irradiance conditions.
• QIOA presents a promising tool for researchers and engineers in the field of solar energy.