Voltage root mean square error calculation for solar cell parameter estimation: A novel g-function approach

Area of Science:

• Renewable Energy
• Photovoltaics
• Electrical Engineering

Background:

• Current solar cell parameter estimation primarily minimizes Root-Mean-Square Error (RMSE) based on current-voltage (I-U) relationships using the Lambert W function.
• Existing methods often overlook voltage-based error metrics, potentially limiting model accuracy and applicability.

Purpose of the Study:

• To introduce a novel analytical solution for calculating RMSE based on measured and estimated solar cell voltages (RMSE_U).
• To develop and present original formulas for Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for solar cell voltages using the g-function.
• To propose and evaluate a new metaheuristic algorithm, the Chaotic Walrus Optimization Algorithm (Chaotic-WaOA), for solar cell parameter estimation.

Main Methods:

• Derivation of an analytical solution for RMSE_U using the g-function (LogWright function), enabling U-I relationship representation.
• Formulation of MAE and MAPE equations for solar cell voltages based on the g-function.
• Application of the Chaotic Walrus Optimization Algorithm (Chaotic-WaOA) for estimating solar cell parameters to minimize RMSE_U.
• Experimental validation using RTC France and SOLAREX MSX-60 PV solar modules, a 60Wp monocrystalline module, a Cadmium telluride (CdTe) module, and a multi-crystalline silicon (mSi) module.

Main Results:

• The proposed RMSE_U calculation method, derived via the g-function, demonstrates numerical applicability and accuracy.
• The Chaotic Walrus Optimization Algorithm (Chaotic-WaOA) effectively estimates solar cell parameters, achieving minimal RMSE_U.
• Experimental verification across diverse solar cell types and outdoor conditions confirms the proposed methods' accuracy and feasibility.
• The study confirms the utility of the g-function for invertible solar cell modeling.

Conclusions:

• The novel RMSE_U metric and g-function-based approach offer a valuable alternative for solar cell parameter estimation.
• The Chaotic Walrus Optimization Algorithm (Chaotic-WaOA) presents a robust and accurate metaheuristic for photovoltaic parameter identification.
• The findings enhance the understanding and modeling of solar cells, contributing to improved photovoltaic system performance and design.