Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling
The transformer health index (HI) concept has been used as an important part of management resources and is implemented for the state assessment and ranking of Power transformers. The HI state is estimated based on many power transformer oil parameters. However, the main problem in the HI procedure...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/11/2405 |
| _version_ | 1797597747785760768 |
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| author | Ibrahim B. M. Taha |
| author_facet | Ibrahim B. M. Taha |
| author_sort | Ibrahim B. M. Taha |
| collection | DOAJ |
| description | The transformer health index (HI) concept has been used as an important part of management resources and is implemented for the state assessment and ranking of Power transformers. The HI state is estimated based on many power transformer oil parameters. However, the main problem in the HI procedure as a diagnostic method is the presence of routine measurements and accurate test results. The power transformer HI prediction is carried out in this work using 1361 dataset samples collected from two different utilities. The proposed model is used to predict and diagnose the HI state of the power transformer by using a convolutional neural network (CNN) approach. The imbalance between the training dataset sample classes produces a good prediction of the class with a major number of samples while a low detection of the class has a minor number of samples. The oversampling approach is used to balance the training samples to enhance the prediction accuracy of the classification methods. The proposed CNN model predicts the HI of the power transformers after applying the oversampling approach to the training dataset samples. The results obtained with the proposed CNN model are compared with those obtained with the optimized machine learning (ML) classification methods with the superiority of the CNN results. Feature reductions are applied to minimize testing time, effort, and costs. Finally, the proposed CNN model is checked with uncertain noise in full and reduced features of up to ±25% with a good prediction diagnosis of the power transformer HI. |
| first_indexed | 2024-03-11T03:08:54Z |
| format | Article |
| id | doaj.art-b9dcf2c8f5b142ffb8961c26ab52f99c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:08:54Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b9dcf2c8f5b142ffb8961c26ab52f99c2023-11-18T07:44:29ZengMDPI AGElectronics2079-92922023-05-011211240510.3390/electronics12112405Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data OversamplingIbrahim B. M. Taha0Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaThe transformer health index (HI) concept has been used as an important part of management resources and is implemented for the state assessment and ranking of Power transformers. The HI state is estimated based on many power transformer oil parameters. However, the main problem in the HI procedure as a diagnostic method is the presence of routine measurements and accurate test results. The power transformer HI prediction is carried out in this work using 1361 dataset samples collected from two different utilities. The proposed model is used to predict and diagnose the HI state of the power transformer by using a convolutional neural network (CNN) approach. The imbalance between the training dataset sample classes produces a good prediction of the class with a major number of samples while a low detection of the class has a minor number of samples. The oversampling approach is used to balance the training samples to enhance the prediction accuracy of the classification methods. The proposed CNN model predicts the HI of the power transformers after applying the oversampling approach to the training dataset samples. The results obtained with the proposed CNN model are compared with those obtained with the optimized machine learning (ML) classification methods with the superiority of the CNN results. Feature reductions are applied to minimize testing time, effort, and costs. Finally, the proposed CNN model is checked with uncertain noise in full and reduced features of up to ±25% with a good prediction diagnosis of the power transformer HI.https://www.mdpi.com/2079-9292/12/11/2405transformer health indextransformer testsCNNML classification methods |
| spellingShingle | Ibrahim B. M. Taha Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling Electronics transformer health index transformer tests CNN ML classification methods |
| title | Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling |
| title_full | Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling |
| title_fullStr | Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling |
| title_full_unstemmed | Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling |
| title_short | Power Transformers Health Index Enhancement Based on Convolutional Neural Network after Applying Imbalanced-Data Oversampling |
| title_sort | power transformers health index enhancement based on convolutional neural network after applying imbalanced data oversampling |
| topic | transformer health index transformer tests CNN ML classification methods |
| url | https://www.mdpi.com/2079-9292/12/11/2405 |
| work_keys_str_mv | AT ibrahimbmtaha powertransformershealthindexenhancementbasedonconvolutionalneuralnetworkafterapplyingimbalanceddataoversampling |