An efficient data sheet based parameter estimation technique of solar PV.

Area of Science:

• Renewable Energy
• Electrical Engineering
• Computational Methods

Background:

• Accurate solar photovoltaic (PV) model parameter estimation is crucial for performance analysis and grid integration.
• Existing methods often require extensive data or complex procedures, limiting their practical application.

Purpose of the Study:

• To develop an efficient and precise parameter estimation technique for solar PV models using only manufacturer datasheet information.
• To compare the performance of Particle Swarm Optimization (PSO) and Harmony Search (HS) for this parameter estimation task.

Main Methods:

• A nonlinear least square objective function was formulated using manufacturer datasheet variables.
• Particle Swarm Optimization (PSO) and Harmony Search (HS) algorithms were applied to optimize the objective function.
• Performance indices, including percentage maximum power deviation index (%MPDI) and overall model deviation index (OMDI), were used for evaluation.

Main Results:

• The developed technique with PSO demonstrated superior performance over HS, achieving %MPDI of 0.0214% and OMDI of 0.213.
• Comparative analysis showed the PSO-based method yielded smaller %MPDI (0.0041%) and OMDI (0.005) than existing methods.
• The technique exhibited rapid convergence, completing estimation in just 5 seconds, and requires minimal data.

Conclusions:

• The proposed parameter estimation technique, leveraging manufacturer datasheets and PSO, is highly efficient, accurate, and fast.
• This data-efficient approach simplifies the process of obtaining accurate solar PV model parameters, enhancing their practical utility.