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Incipient faults usually emerge from partial discharges which eventually cause insulation degradation between two insulated cable cores. Early detection of incipient faults is of particular importance because insulation defects caused by incipient faults may lead to permanent faults in underground distribution networks. This paper presents a novel approach using S-transform and support vector regression to predict the occurrence of incipient faults in underground cable networks. The results of this study proved that the new approach is capable to predict the existence of partial discharges in underground power cable systems in Malaysia.

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This paper presents a smart power quality data a analyzer (S-PQDA) or power quality diagnosis software (PQDS) tool that performs power quality (PQ) diagnosis on the PQ disturbance data recorded by an online PQ monitoring system. The software tool enables power utility engineers to perform automatic PQ disturbance detection, classification and diagnosis of the disturbances. The PQDS also assists the power utility engineers in identifying the existence of incipient faults due to partial discharges in the cable compartment. The overall accuracy of the software in performing PQ diagnosis is 96.4%.