Robust detection of acoustic partial discharge signals in noisy environments
المؤلف | Hussein, Ramy |
المؤلف | Bashir Shaban, Khaled |
المؤلف | El-Hag, Ayman H. |
تاريخ الإتاحة | 2021-01-27T11:06:53Z |
تاريخ النشر | 2017 |
اسم المنشور | I2MTC 2017 - 2017 IEEE International Instrumentation and Measurement Technology Conference, Proceedings |
المصدر | Scopus |
الملخص | Partial discharge (PD) can be used to predict insulation failures in power transformers. Accurate detection of particular PD types has a significant role in anticipating forthcoming outages. However, the noise encountered with PD measurements negatively affects the detection accuracy. In this paper, we propose a robust PD detection technique that is immune to noise through efficient frequency domain-based feature extraction from acoustic emission signals. The PD spectrum is first obtained using Fourier transform and then, the low frequency band of 0.05-0.15MHz is used as a representative feature vector. Finally, four different classifiers are used to examine the PD detection accuracy. Experimental results on a benchmark dataset verify the robustness of the proposed method for PD detection, as it achieves 100% classification accuracy for clean PD signals and up to 99.62% for noisy PD signals. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Detection Feature extraction Noise Partial discharge Random forest |
النوع | Conference Paper |
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