Aiding Prosumers by Solar Cell Parameter Optimization Using a Hybrid Technique for Achieving Near Realistic P-V Characteristics

Abstract

The correct optimization of the solar cell electrical-model parameters is the key to produce better and more realistic P-V characteristics. This helps prosumers to select solar panels having better comparative efficiency, which in turn increases electricity production. Evolutionary Algorithms have shown comparatively good results in the estimation of these parameters. The Photon-current, Diode dark saturation current, Series resistance, Shunt resistance and Diode ideality factor, constitute the single diode's unknown electrical model parameters. The mathematical-model of the P-V cell is derived in terms of Series-resistance and Diode-ideality factor. These two parameters are then used in a 2-variable single objective function. Using this derived model, Genetic Algorithm and Numerical method, a new parameter estimation technique has been proposed. Making use of machine learning and combination of two algorithms highlights the usefulness of the intended hybrid technique. P-V characteristic and relative maximum power point error of different solar cells, have been compared. The relative analysis disclosed that the proposed method offers more pragmatic P-V characteristics, as compared to the existing methods.

Keywords

• Genetic algorithm (GA)
• prosumers
• maximum power point error (MPPE)
• least square algorithm (LSA)
• parameter estimation (PE)
• meta-heuristic