Abstract:
The reliability issues of magnetic elements become more and more prominent with the wide-range application of high-power-density power electronics. Normally, high-frequen...Show MoreMetadata
Abstract:
The reliability issues of magnetic elements become more and more prominent with the wide-range application of high-power-density power electronics. Normally, high-frequency planar transformers take up above 30% of the weight and volume of the converter. They suffer various reliability stresses, such as high operating temperature and high-frequency voltages, which can lead to parameter degradations and even failure during operation. To achieve a reliability-oriented design and minimal lifetime maintenance cost of the high-power-density converter, the lifetime prediction of planar magnetics is essential. This paper proposes a method to predict the parameter of planar transformers under thermal reliability stress using a deep learning algorithm. A deep learning neural network is established using the existing parameter shift data in the accelerated aging test. Then, based on the Bi-LSTM model, the future parameter shift of the planar transformer is predicted. Finally, multiple methods are compared to show the advantage of the proposed method. Compared with the conventional curve fitting method, the deep learning algorithm is more suitable for lifetime prediction in terms of filtering data, considering the weight factor, and predicting future change trends.
Published in: CPSS Transactions on Power Electronics and Applications ( Volume: 8, Issue: 1, March 2023)
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- IEEE Keywords
- Index Terms
- Planar Transformer ,
- BiLSTM Algorithm ,
- Neural Network ,
- Deep Learning ,
- Thermal Stress ,
- Power Electronics ,
- Accelerated Aging Test ,
- Prediction Model ,
- Time Series ,
- Root Mean Square Error ,
- Mean Square Error ,
- Convolutional Neural Network ,
- Measurement Values ,
- Time Series Data ,
- Reliability Test ,
- Mean Absolute Error ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Mean Absolute Percentage Error ,
- Magnetic Components ,
- Long Short-term Memory Model ,
- Power Electronic Devices ,
- Short-term Memory Network ,
- Drift Parameter ,
- Primary Resistance ,
- Dielectric Capacitors ,
- Time Series Prediction ,
- Short-term Memory ,
- Hyperbolic Tangent ,
- Thermal Shock
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Planar Transformer ,
- BiLSTM Algorithm ,
- Neural Network ,
- Deep Learning ,
- Thermal Stress ,
- Power Electronics ,
- Accelerated Aging Test ,
- Prediction Model ,
- Time Series ,
- Root Mean Square Error ,
- Mean Square Error ,
- Convolutional Neural Network ,
- Measurement Values ,
- Time Series Data ,
- Reliability Test ,
- Mean Absolute Error ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Mean Absolute Percentage Error ,
- Magnetic Components ,
- Long Short-term Memory Model ,
- Power Electronic Devices ,
- Short-term Memory Network ,
- Drift Parameter ,
- Primary Resistance ,
- Dielectric Capacitors ,
- Time Series Prediction ,
- Short-term Memory ,
- Hyperbolic Tangent ,
- Thermal Shock
- Author Keywords