Publication: Multiple partial discharge source discrimination with multiclass support vector machines
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2016-08-15
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Elsevier
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Abstract
The 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.
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Keywords
Support vector machine, Partial discharges, Electric maintenance, Machine learning, Condition monitoring, Risk assessment, Pattern-recognition, Neural-network, Pd sources, Classification, Separation, Apparatus, Voltage
Bibliographic citation
Robles, 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.