A Genetic-Algorithm Support Vector Machine and D-S Evidence Theory Based Fault Diagnostic Model for Transmission Line

摘要

In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and the D-S evidence theory based fault diagnostic model is proposed. Two genetic-algorithm support vector machine based diagnosis boxes separately identify the fault types to get two kinds of preliminary diagnostic results according to the voltage and current phasors data from the phasor measurement units. Then, a deviation coefficient is derived to represent the diagnostic conflicts existing in the two kinds of preliminary diagnostic results. The mass function of the D-S evidence theory is updated by using the deviation coefficient to eliminate the diagnostic conflicts and improve the diagnosis accuracy. Finally, the proposed diagnostic model was applied in an actual power system in Hubei province in China. The simulation results and practical results indicated the feasibility and the effectiveness of the diagnostic model.