Applying Machine Learning to Enhancement the Level Energy-Based Smart Grid by the Renewable Environment
Osman Nuri UÇAN1, Ghaith Thaaer Fadhil Aldoori2, Alaa Hamid Mohammed3
1Prof. Dr. Osman Nuri UÇAN, Department of Electrical and Computer Engineering, Altinbas University Turkey.
2Ghaith Thaaer Fadhil Aldoori, Department of Electrical and Computer Engineering Altinbas University, Turkey.
3Alaa Hamid Mohammed, Department of Electrical and Computer Engineering, Altinbas University, Turkey.
Manuscript received on 09 October 2021 | Revised Manuscript received on 25 October 2021 | Manuscript Accepted on 15 November 2021 | Manuscript published on 30 November 2021 | PP: 1-6 | Volume-1 Issue-1, November 2021 | Retrieval Number:100.1/ijeer.A1001121121
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Abstract: Beneficent the power grid (PG) of the electrical system and solar freightage rectifiers stay a confront. We intend to suggest the optimal design of the intelligent system with a smart grid for the efficient operation of the energy management system. This design is based on an interactive combination of machine learning with robust neural networks and control circuits. The best parameters of power grid and robust control are determined via optimization, where NN is tuned using genetic algorithm to achieve the optimal solution. Neural Network is used to enhance the robust control parameters for designing NN of the Machine learning system. The entire scheme is further tuned by hybrid energy parameters under various operating conditions to improve the power grid management performance in terms of charging and rectifying. Performing the proposed analog-implemented energy management controller is evaluated by interfacing it with a hardware prototype experimental application of dual photovoltaic (PV) system.
Keywords: Machine learning, Smart Grid, Energy level, RBF-ANN.