A Review of Test Voltages Used in Partial Discharge Measurements

Venge, Tapiwa; Nyamupangedengu, Cuthbert

doi:10.1109/africon51333.2021.9570871

Cited by 1 publication

(1 citation statement)

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“…ONPD detection is conducted while the equipment is live, requiring no de-energization or disruption of normal operation [ 3 , 4 , 5 ]; hence, the equipment is subjected to actual conditions that can affect PD activity such as HV stress, temperature, and vibration. On the other hand, OFPD detection is conducted with the equipment disconnected from the network, bringing a complete halt to normal operation [ 6 , 7 , 8 ], and requires the equipment to be energized via external means. OFPD can incur additional costs compared to ONPD because it requires the interruption of service, which can halt the operation of critical utilities that operate continuously.…”

Section: Introductionmentioning

confidence: 99%

Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching

Abubakar,

Zachariades

2024

Sensors

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This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

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