Convolutional-Neural-Network-Based Partial Discharge Diagnosis for Power Transformer Using UHF Sensor
| Autor: | Yong-Hwa Kim, Jong-Ho Sun, Yong-Sung Cho, Vo-Nguyen Tuyet-Doan, The-Duong Do |
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| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science power transformer 02 engineering and technology Fault (power engineering) 01 natural sciences Convolutional neural network law.invention law 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Electronic engineering General Materials Science Transformer 010302 applied physics convolutional neural network (CNN) business.industry Deep learning General Engineering fault diagnosis Partial discharge (PD) Ultra high frequency Partial discharge Feedforward neural network 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence Electricity business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 207377-207388 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase-amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%. |
| Databáze: | OpenAIRE |
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