Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN
Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/22/8587 |
| _version_ | 1797465423927574528 |
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| author | Jingmin Fan Huidong Shao Yunfei Cao Lutao Feng Jianpei Chen Anbo Meng Hao Yin |
| author_facet | Jingmin Fan Huidong Shao Yunfei Cao Lutao Feng Jianpei Chen Anbo Meng Hao Yin |
| author_sort | Jingmin Fan |
| collection | DOAJ |
| description | Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting. |
| first_indexed | 2024-03-09T18:21:15Z |
| format | Article |
| id | doaj.art-1b395a9eb33c4f3eb36638343b171c91 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T18:21:15Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1b395a9eb33c4f3eb36638343b171c912023-11-24T08:15:24ZengMDPI AGEnergies1996-10732022-11-011522858710.3390/en15228587Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANNJingmin Fan0Huidong Shao1Yunfei Cao2Lutao Feng3Jianpei Chen4Anbo Meng5Hao Yin6School of Automation, Guangdong University of Technology, Guangzhou 510012, ChinaGuangdong Tianlian Electric Power Design Co., Ltd., Guangzhou 510700, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510012, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510012, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510012, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510012, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510012, ChinaPower transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting.https://www.mdpi.com/1996-1073/15/22/8587online DGA monitorensemble learningCSO-NNfault diagnosisfaults prediction |
| spellingShingle | Jingmin Fan Huidong Shao Yunfei Cao Lutao Feng Jianpei Chen Anbo Meng Hao Yin Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN Energies online DGA monitor ensemble learning CSO-NN fault diagnosis faults prediction |
| title | Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN |
| title_full | Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN |
| title_fullStr | Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN |
| title_full_unstemmed | Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN |
| title_short | Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN |
| title_sort | condition forecasting of a power transformer based on an online monitor with el cso ann |
| topic | online DGA monitor ensemble learning CSO-NN fault diagnosis faults prediction |
| url | https://www.mdpi.com/1996-1073/15/22/8587 |
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