Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy
Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/19/9/439 |
| _version_ | 1798040705092812800 |
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| author | Haikun Shang Kwok Lun Lo Feng Li |
| author_facet | Haikun Shang Kwok Lun Lo Feng Li |
| author_sort | Haikun Shang |
| collection | DOAJ |
| description | Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy. |
| first_indexed | 2024-04-11T22:11:16Z |
| format | Article |
| id | doaj.art-f0c9de5f053b42d3a15a675b435ad298 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T22:11:16Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f0c9de5f053b42d3a15a675b435ad2982022-12-22T04:00:34ZengMDPI AGEntropy1099-43002017-08-0119943910.3390/e19090439e19090439Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample EntropyHaikun Shang0Kwok Lun Lo1Feng Li2School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaPower Systems Research Group, University of Strathclyde, Glasgow G1 1XW, UKState Grid Electric Power Research Institute, Urumqi 830011, ChinaPartial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.https://www.mdpi.com/1099-4300/19/9/439partial dischargefeature extractionensemble empirical mode decompositionsample entropyrelevance vector machine |
| spellingShingle | Haikun Shang Kwok Lun Lo Feng Li Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy Entropy partial discharge feature extraction ensemble empirical mode decomposition sample entropy relevance vector machine |
| title | Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy |
| title_full | Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy |
| title_fullStr | Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy |
| title_full_unstemmed | Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy |
| title_short | Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy |
| title_sort | partial discharge feature extraction based on ensemble empirical mode decomposition and sample entropy |
| topic | partial discharge feature extraction ensemble empirical mode decomposition sample entropy relevance vector machine |
| url | https://www.mdpi.com/1099-4300/19/9/439 |
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