Improved grey wolf optimization for parameter extraction of polycrystalline and mono crystalline solar PV panels using double diode model

The shift from traditional energy sources towards sustainable ones has become necessary with the increase in carbon emissions and the decay of fossil fuels, which lead to global warming and climate change. Solar power generation has been a popular option among other sustainable energies. For generation of solar power photovoltaic cells are required. The performance of photovoltaic cells plays a vital role in power generation. Therefore these cells need to be modeled effectively. Double-diode model is more accurate than the single-diode model because it considers both radioactive and non-radioactive recombination losses. This model improves the analysis of photovoltaic systems because it more closely matches experimental data. However, a lack of I-V characteristic data supplied by PV panel manufacturers often restricts the parameter assessment required for optimal performance, making it difficult to derive the whole seven-parameter of double-diode model through conventional techniques. Hence it recommends metaheuristic techniques for accurate parameter extraction from datasheets of PV panels. In this paper an enhanced version of the Grey Wolf Optimizer (GWO) algorithm called Improved Grey Wolf Optimizer (IGWO) has been proposed for extraction of parameters. It strikes the right balance between exploration and exploitation to enhance the convergence speed, accuracy, and reliability. The proposed algorithm minimizes the sum of squared errors at critical operating points, including the open-circuit point, short-circuit point, and maximum power point to extract the optimized parameters. In this paper the parameter extraction procedure is applied to four PV module samples: the poly-crystalline Kyocera KC200GT, TSM250P, Shell S75 and the mono-crystalline Shell SQ85. Graphical abstrac t