TY - JOUR
AU1 - Priya, G. Suriya
AU2 - Geethanjali, M.
AU3 - Devi, M. Meenakshi
AB - Electric power network transmission lines frequently experience various kinds of failures. To avoid severe malfunctions and limit the adverse effects caused by transmission line faults, protective systems must be able to detect fault states and abnormalities. This paper proposes an intelligent supervised learning-based fault diagnosis approach to recognise fault type and fault location data even in the case of system disturbance. Initially, a Decision Tree Fault Detector (DTFD) algorithm is proposed to find out the various kinds of faults that occur in electrical networks. To enhance the decision-making process of the distance relay, it is advisable to employ a Support Vector Machine (SVM) as a Fault Location Estimator (FLE) protection mechanism. The system can accurately and proficiently detect any type of malfunction on the transmission line, while also offering line protection. This active combination increases the speed and accuracy of fault management in power systems by addressing the shortcomings of traditional approaches. The MATLAB simulation package is used to replicate the modified IEEE 13 bus test feeder with the insertion of Renewable Distributed Energy Resources (RDERs). The reliability and stability of grid-connected RDERs are improved by the investigation of the suggested intelligent protection scheme, which shows satisfactory results.
TI - Active Protection Strategy for Transmission Networks with Wind Distributed Generation Systems Based on SVM
JF - Journal of Electrical Engineering and Technology
DO - 10.1007/s42835-025-02168-8
DA - 2025-05-01
UR - https://www.deepdyve.com/lp/springer-journals/active-protection-strategy-for-transmission-networks-with-wind-mhzMSGun14
SP - 2065
EP - 2074
VL - 20
IS - 4
DP - DeepDyve
ER -