Fault Monitoring and Analysis of Distributed New Energy Grids Using Simulation Data Models

Data analysis is essential for fault identification and detection in smart grids in order to maintain grid monitoring. Many DL algorithms have been developed recently for data analysis applications related to smart grids. To resolve these challenges, the study suggests a Deep Neural Network (DNN) for data-driven fault location detection and type of fault classification by exploiting the Modified Sand Cat Swarm Optimization (MSCSO) optimization. Here, the DNN is used to diagnose the faults and determine the position of the sites. Numerous synthetic field data sets derived from simulated models of different transmission line types are used for training and testing. The position and type of faults are predicted by the DNN classifier based on the fault signal features, in which the DNN weights are ideally tuned using a novel MSCSO optimization method that is the improved concept of Sand Cat Swarm Optimization (SCSO). Lastly, MATLAB/Simulink is used to implement the suggested DNN-based method for fault classification and its localization in transmission line. An intellectual IEEE 6-node network model is utilized to confirm the efficacy and reliability of these methods. The outcomes demonstrated its effectiveness in giving the system operator precise and detailed analytics for fault identification.