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.
Highlights
To cope with the expanding requirements of electrical energy, solar photo-voltaic cells have gained a noteworthy research attention to amplify its application
All these approaches lead to a less accurate estimation of maximum power point error (MPPE) [9], [21].To increase the accuracy of results, this paper considers all five electrical model parameters
At first the Characteristic equation of the Solar PV in terms of two unknowns, namely Series Resistance and Diode Ideality Factor has been derived in this paper
Summary
To cope with the expanding requirements of electrical energy, solar photo-voltaic cells have gained a noteworthy research attention to amplify its application. On contrary to the Analytical method, each sample points from the characteristic curve of the solar cell are considered, when using numerical methods This provides more accurate results in comparison to the Analytical methods [3], [4]. Computational efforts have been reduced in some of the research works by neglecting Shunt resistance “Rsh” [19], [ 20], Series Resistance “Rs” [17], [18] or by assuming the ideality factor “n” of the diode [3] All these approaches lead to a less accurate estimation of MPPE [9], [21].To increase the accuracy of results, this paper considers all five electrical model parameters. It is clear from the results that using Least Square Method simultaneously with genetic algorithm helps to achieve greater accuracy in results and computational inefficiencies are reduced
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