Abstract
Introduction:: A hybrid power generating system, which is mostly meant for its reliable performance to meet the current energy demand, is a renowned renewable energy. To overcome issues like the inability to handle the blackout problem, avoidance of renewable factors, and frequency instability of the power grid, a Hybrid Power Generation System (HPGS) utilizing biomass and wind is proposed in this paper. Method:: With the combination of controllers as well as inverters and blackout controllers, this work aims to maximize power with minimum cost. The DC-DC converter controlled by Renyi’s Quadratic Entropy-Based Neuro Proportional-Integral-Derivative (RQEB-NPID) and the inverter under the control of the Volt/VAR controller are utilized as a major contribution. Result:: This, in turn reduces the frequency instability problem. Moreover, the renewable factor is considered for reducing the cost of energy using the Pythagorean Fuzzy-based Equilibrium Optimizer (PF-EO)-based inverter. Next, by utilizing the Attention-based Rectified Linear Unitactivated Artificial Neural Network (ARELU-ANN) controller, the blackout problem that interrupts the power supply to the load is eliminated with the highest accuracy of 7.69%. Conclusion:: Also, the proposed ARELU-ANN attained the highest specificity and precision of 9.80% and 6.17 %, respectively. Lastly, a comparative analysis is performed, which demonstrates that the proposed solution is optimum for supplying maximum energy at the least cost.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.