Robles Muñoz, GuillermoParrado Hernández, EmilioArdila Rey, Jorge AlfredoMartínez Tarifa, Juan Manuel2022-09-052022-09-052016-08-15Robles, G., Parrado-Hernández, E., Ardila-Rey, J., & Martínez-Tarifa, J. M. (2016). Multiple partial discharge source discrimination with multiclass support vector machines. Expert Systems with Applications, 55, 417–428.0957-4174https://hdl.handle.net/10016/35636The costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses.12eng© 2016 Elsevier Ltd. All rights reservedAtribución-NoComercial-SinDerivadas 3.0 EspañaSupport vector machinePartial dischargesElectric maintenanceMachine learningCondition monitoringRisk assessmentPattern-recognitionNeural-networkPd sourcesClassificationSeparationApparatusVoltageresearch articleIngeniería Mecánicahttps://doi.org/10.1016/j.eswa.2016.02.014open access417428Expert Systems with Applications55AR/0000018672