A Frequency Regulation of an Isolated Distributed Generation with Electric Vehicle and Battery Management System Integration Using Artificial Neural Network Technique

The rising mix of environmentally friendly power sources in dispersed age frameworks requires hearty recurrence guideline systems to keep up with lattice solidity. This paper presents a clever way to address this test by coordinating Electric Vehicles (EVs) and Battery Management Systems (BMS) into a separate, disseminated age framework. The center of the proposed framework lies in the use of Artificial Neural Networks (ANN) for continuous dynamics in recurrence guidelines. The framework parts incorporate sustainable sources, a circulated age unit, energy capacity as batteries, and electric vehicles. The MATLAB/Simulink stage is utilized for the demonstration and reproduction of the whole framework. The battery-board framework guarantees proficient charging and releasing of batteries, while electric vehicles add to the general framework elements. An Artificial Neural Network (ANN) is used to foresee and control recurrence deviations. The brain network is prepared utilizing a data set produced from different working circumstances. Input highlights to the brain network incorporate power yields from sustainable sources, battery State of Charge (SOC), and other pertinent boundaries, with the result being the recurrence deviation. Recreation results exhibit the viability of the proposed framework in keeping up with recurrence security under shifting circumstances. The mix of EVs and BMS, combined with the prescient capacities of the brain organization, adds to further development of constant recurrence guideline in separated, dispersed age frameworks. The introduced approach gives a promising answer for improving the dependability and execution of future environmentally friendly power frameworks.