Estimating equivalent circuit parameters in various photovoltaic models and modules using the dingo optimization algorithm

While the demand for electrical energy in the world increases daily, a large part of this demand is still provided by fossil fuels. However, the most significant contribution to solving the economic and environmental problems that arise is the spread of renewable energy production systems. Solar power generation systems are one of these renewable energy generation systems. In this study, cell and module parameters are modeled and estimated in different ways to obtain maximum energy from solar cells used in solar power generation systems. Cell and model vendors need to provide complete information to the end user. Therefore, the systems created turn into a nonlinear problem with many unknown parameters. In this study, single-diode model (SDM), double-diode model (DDM), and triple diode model (TDM) for photovoltaic (PV) cells as well as parameter estimations of four different PV modules produced by other vendors were performed for the first time with the dingo optimization algorithm (DOA). The mathematical model of PV module parameters is derived using open-circuit voltage (V_oc), short-circuit current (I_sc), and maximum power point values (P_mpp). The parameter values obtained by the algorithm aim to get the maximum power point curve for each model and module with minimum error. These values are compared with five traditional and five recent meta-heuristic algorithms, which have extreme positions in the literature.