article · Electronics
A modified metaheuristic algorithm known as the Modified Dwarf Mongoose Optimizer provides improved modelling and parameter extraction for solar photovoltaic systems. Rooted in the social foraging behaviour of dwarf mongooses, the approach introduces an alpha-directed knowledge-gaining strategy that enhances search performance. Testing was conducted on single-, double-, and triple-diode versions of two standard solar modules, namely Kyocera KC200GT and R.T.C. France. Evaluation based on root mean square error minimisation revealed substantial performance gains over the baseline algorithm. For the Kyocera module, the modified approach improved average efficiency by up to 91.7 percent. For the R.T.C. France module, it achieved success rates between 66.67 and 100 percent across diode models, demonstrating superior accuracy in electrical parameter estimation.
Precise electrical parameter extraction is vital for accurately simulating and designing solar photovoltaic installations. By lowering error in solar cell models, computational tools can better forecast energy yield and electrical behaviour under real conditions, ultimately helping engineers develop more reliable renewable energy systems.
The algorithm could be utilised by solar system designers and photovoltaic software developers seeking precise mathematical representations of solar panels. Currently at an applied simulation stage, the technique has been verified on established commercial modules. Progression toward commercial use would require embedding the optimisation code into solar engineering design software or asset management platforms to assist in automated diagnostic and modelling workflows.
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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.
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DOI: 10.3390/electronics12244990
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