Adaptive Smart Energy Management for Wind-Battery Systems Considering Grid Fault Scenarios
DOI:
https://doi.org/10.12928/biste.v8i4.16033Keywords:
Maximum Power Point, Wind Generator, Battery Storage, Grid-Connected & Stand-Alone Modes, Energy Management, Radial Basis Function Neural NetworkAbstract
This work introduces a novel energy management system based on radial basis function neural network (RBFNN) adapted for both grid-connected and segmented modes. The system components are electrical distribution grid, wind generator, power electronic converters and batteries. The study uses intelligent control and prevents the spread of potential problems or disturbances. This system incorporates various controllers responsible of specific functions like MPP monitoring, of battery charging and discharging, and of an inverter for effectively managing the transition between energy sources based on load requirements and available sources operating at their MPP. The objectives are to facilitate coordinated operation among distributed energy resources, ensuring the provision of necessary active power and additional services as necessary. The simulation uses MATLAB/Simulink environment. Simulation results demonstrate the effectiveness and feasibility of the proposed strategy. Overall, the obtained results affirm the practicality and advantages of employing neural networks in energy management systems.
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Copyright (c) 2026 Habib Chaib, Housseyn Chaib, Belkacem Belabbas, Fajer M. Alelaj, Alfian Ma’arif, Mohamed Metwally Mahmoud, Vojtech Blazek, Shady M. Sadek
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