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Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering.

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Summary
This summary is machine-generated.

Accurately locating partial discharge (PD) events in transformers is crucial for preventing failures. This study introduces new algorithms for precise PD source localization using ultra-high frequency sensor data, improving accuracy significantly, especially for distant events.

Keywords:
FDTD methodsUHF measurementsnonlinear wave propagationpartial dischargessource locationtime of arrival estimationwaveform analysis

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Condition Monitoring

Background:

  • Transformer insulation failure is a major cause of electrical grid disruptions.
  • Partial discharge (PD) is a key indicator of insulation degradation.
  • Accurate localization of PD events is essential for timely maintenance and preventing catastrophic failures.

Purpose of the Study:

  • To develop and validate advanced algorithms for pinpointing the location of partial discharge (PD) events within transformers.
  • To improve the accuracy and reliability of PD source localization using ultra-high frequency (UHF) sensor data.
  • To offer a more effective diagnostic tool for transformer health assessment.

Main Methods:

  • Review of existing signal arrival time determination and source localization techniques for PD events.
  • Proposal of a novel Convolutional Iterative Filter with Thresholding (CIFT) algorithm for precise signal arrival time detection.
  • Development of a reference table of travel times for accurate PD source location resolution.
  • Experimental validation using laboratory-generated PD events and simulated data from operational transformers.

Main Results:

  • The proposed CIFT algorithm demonstrates superior performance in determining PD signal arrival times.
  • The new localization approach significantly outperforms existing methods in accuracy.
  • Location accuracy improvements are particularly pronounced (up to 3.7X) for PD events originating far from the UHF sensors.
  • The methodology proves effective for both laboratory and simulated real-world transformer conditions.

Conclusions:

  • The developed algorithms offer a substantial advancement in the capability to locate partial discharge sources in transformers.
  • This enhanced localization accuracy enables more effective and targeted maintenance strategies, reducing transformer downtime.
  • The proposed method represents a significant step forward in non-invasive transformer condition monitoring and fault prediction.