Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

Abstract

This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.

Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem

“…To establish objective functions that are suitable for a number of computational methods, it must first be possible to quantify the output voltage and current that is produced in each of the models [41][42][43]. As a result, the function's target may be to find the disparity between the current that is generated in the model that has been developed and the experimental current.…”

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

“…Utilized across diverse engineering disciplines, the applications of contemporary evolutionary optimization algorithms have been effectively drawn for several renewable energy and power system tasks. In modeling of solar cells, an improved Archimedes Optimization Algorithm [13], dwarf mongoose [14], and Modified Rime-Ice Growth optimizer [15] have been performed for determining the accurate parameters of solar cells considering their different equivalent circuits. In addition, in the optimization of wind turbine, using a binary artificial bee colony algorithm with multi-dimensional updates [16], a binary artificial algae algorithm [17], and modified differential evolution algorithm [18] has been illustrated for wind turbine placement.…”