A Novel Intelligent Transmission Line Fault Diagnosis Model Based on EEMD and Multiclass PSVM

摘要: The performance of a power network is frequently affected by the faults occurring in transmission line and for maintaining healthy operation, fault diagnosis necessary. In recent times, significant amount research work has been directed to address this problem techniques such as ANN, WT, FIS, SVM, Decision tree, etc., have already employed. This study presents an intelligent framework classification, applying innovative machine learning algorithm called Proximal Support Vector Machine (PSVM) that requires small training time solve nonlinear problems applicable high-dimension application. EEMD method utilized raw electric signals’ decomposition into Intrinsic Mode Functions (IMFs), which act input variable PSVM-based classifier identification faults. Simulations evaluations suggest proposed scheme effective, reliable, accurate.