Classification of faults in transmission line with penetration of WDG using ANN

• Proposed an ANN-based model for fault classification in wind-integrated grids • Voltage signals at PCC used for training and testing under 1,200 fault cases • Achieved 95.6% classification accuracy for line-to-line (L–L) fault detection • Model demonstrates robustness under varying wind penetration conditions • Supports SDG 7 by improving renewable-integrated transmission line reliability Distributed generation (DG) plays a key role in addressing the increasing power demand within modern power systems. The integration of distributed generators into the grid raises several concerns about power system structure regulation and protection. The increasing penetration of wind-based distributed generation challenges conventional fault detection techniques in transmission systems. Even after years of extensive research, fault classification continues to be one of the most important difficulties among the several fault concerns that arise in a distributed generation integrated transmission line. To prevent financial losses, researchers are working to identify and analyze these problems at the earliest. This paper attempts to examine the type of fault that occurred in the Wind-Integrated Transmission Lines. This study proposes Artificial neural network-based techniques to identify transmission line faults in the presence of Wind distributed generation (WDG). The voltage signal as an input for artificial neural network (ANN) is taken out at the point of common connection (PCC). Training and testing are performed using the extracted data, with 70% allocated for training and 30% for testing. Faults in the simulated model are classified using an ANN technique. To model and simulate the proposed WDG integration approach, the MATLAB/Simulink environment is utilized. The findings are then examined and compared with and without penetration of WDG. The proposed model achieved 95.6% classification accuracy for L-L fault across 1,200 fault scenarios. This supports Sustainable Development Goals (SDG) 7 by improving protection reliability in renewable-integrated grids.