Condition Monitoring of Distribution Power Network Assets Using PMU Measurements

The vast number of assets in low-voltage distribution networks poses a considerable challenge for asset management. Historically, the approach to asset management in distribution feeders has been predominantly passive, which involves either allowing assets to operate until failure or conducting periodic field inspections. The integration of dedicated online monitoring sensors into distribution assets has been exceptionally limited. This paper aims to study the feasibility of using phasor measurement unit (PMU) data for real-time asset condition monitoring. Extensive accelerated ageing experiment on a laboratory transformer and the insulator pollution experiment has been performed. The PMU measurements obtained from both experiments were utilized to assess the health of the assets by analysing the standard deviation of the signal-to-noise ratio (SNR) after statistical analysis and noise segregation of the original PMU data. Moreover, an arcing feature was identified as a precursor to asset failure, leading to the proposal of an innovative approach for online detection of arcing faults based on isolation forest and slope detection. After analysing experimental arcing datasets, the proposed arcing detection method demonstrates an average accuracy of 93.75%. Finally, an implementation platform was developed, enabling real-time data streaming from 100 PMUs for online monitoring of asset conditions.