For the fulfillment of global energy demand, the best options are renewable energy sources due to their ease of availability and non-polluting nature. Hybrid system improves the efficiency of the overall system and provides better balance in energy supply. This study proposes a hybrid bat–dragonfly algorithm for providing optimal power flow in the wind–solar system by tuning the controller parameters. Bat algorithm has the featureless computing time with low accuracy, and dragonfly algorithm has the feature of high accuracy with more computing time. The accuracy of the controller tuning gets improved with less computational time by integrating the operations of both bat and dragonfly algorithms. Fuzzy rationale–based maximum power point tracking extracts the maximum power available in wind–solar system. The results show that the proposed hybrid algorithm provides better execution in the tuning of controller parameters compared with the existing optimization methods with a low level of total harmonic distortion. Furthermore, the proposed hybrid bat–dragonfly algorithm outperforms the benchmark optimization algorithms when tested.
The electricity demand is growing due to increasing population and fast industrial development . To meet the worldwide energy requirement, the greatest possibilities are the wind-solar energy sources due to excessive accessibility, simplicity in use, and non-polluting in nature . The integration of these resources offers higher advantages, but the quality of the power scheme gets influenced by the different characteristics of wind and solar energy. Thus in the proposed work, an augmented controller and rectifier have been designed to improve the power quality in which the source current gets optimized by using the Hybrid Bat-Dragonfly optimization algorithm. The quality of the optimized power will be enhanced through the Five Legged Power Converter (FLPC) by converting the DC into AC by using a three-phase bridge rectifier without any power loss. The occurrences of harmonics in the current are reduced using a sieve optimized algorithm design.
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