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| A Live Detection Technology of Distribution Network Cable Insulation Deterioration State Based on Harmonic Components |
| Xu Haisong1, Zhang Daning1, Hu Ran2, Lu Xu2, Wang Anzhe3, Wang Yuli3, Zhang Guanjun1 |
1. School of Electrical Engineering Xi′an Jiaotong University Xi′an 710049 China;
2. Shenzhen Power Supply Bureau Co. Ltd Shenzhen 518000 China;
3. China Electric Power Research Institute Co. Ltd Wuhan 430000 China |
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Abstract Due to restrictions imposed by power outages for maintenance on the urban power grid, the on-line detection technology of harmonic currents in distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, aiming to enhance the real-time diagnosis of the insulation status of distribution network cables.
In this study, COMSOL finite element software was used to simulate the pores and water tree defects in the XLPE insulation layer of cables. The distribution of magnetic field strength in XLPE cables under different defect conditions was compared and analyzed. A real experimental platform for 10 kV distribution network cables was established, and typical defective cables with moisture and long-term thermal aging were prepared. The induced currents of aging defects and water tree defects in the XLPE cable insulation layer were collected in experiments, and the 2nd to 11th harmonic currents under different insulation defects were extracted. The influence patterns of pore depth caused by aging and external moisture intrusion on induced currents were obtained.
To effectively assess the degree of cable degradation, a cable degradation assessment method based on the harmonic features of induced currents was constructed using LASSO regression. In the prediction analysis of normal thermally aged cables, the root mean square error of LASSO regression was 17.1 days, accounting for 14% of the actual aging time range, indicating high accuracy. The prediction of aging time for moisture-affected cables was longer, consistent with the actual state of accelerated insulation aging due to moisture.
To accurately identify the type of insulation layer defects in cables, a defect identification method combining principal component analysis and clustering algorithm was developed based on the data sample set. When using harmonic data of a 300 A test current for clustering identification of moisture-affected and normal aging cables, the accuracy reached 75.64%, effectively distinguishing between moisture-affected cables and normal cables.
The cable insulation live diagnosis technology proposed in this study, based on the harmonic current characteristics, integrates cable insulation degradation status, induced harmonic current features, and clustering analysis algorithms, achieving intelligent identification of cable insulation defects.
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Received: 14 November 2023
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2024, Vol. 39 
