An independent component analysis algorithm can effectively separate UHF signals from multiple partial discharge sources, improving the on-line monitoring of electrical equipment insulation.
May aid multi-source insulation monitoring; leaves open real-world validation before operational use.
The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect.
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Boya‐Lara et al. (2017) studied this question.
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