Pub Date : 2024-04-16DOI: 10.1109/OJPEL.2024.3389211
Z. Li;L. Wang;R. Liu;R. Mirzadarani;T. Luo;D. Lyu;M. Ghaffarian Niasar;Z. Qin
Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data.
{"title":"A Data-Driven Model for Power Loss Estimation of Magnetic Materials Based on Multi-Objective Optimization and Transfer Learning","authors":"Z. Li;L. Wang;R. Liu;R. Mirzadarani;T. Luo;D. Lyu;M. Ghaffarian Niasar;Z. Qin","doi":"10.1109/OJPEL.2024.3389211","DOIUrl":"10.1109/OJPEL.2024.3389211","url":null,"abstract":"Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data.","PeriodicalId":93182,"journal":{"name":"IEEE open journal of power electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10502151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-16DOI: 10.1109/OJPEL.2024.3389105
Valeriya Titova;Martin Lapke
This study presents a novel, integrated approach to investigating and characterizing the impact of humidity on Insulated-Gate Bipolar Transistors (IGBTs) within large-scale inverter systems. Combining meticulously designed experimental setups and advanced finite element simulations, we delve deep into the complex dynamics of moisture transfer within IGBT modules. Our research demonstrates a meticulously designed experimental setup within a controlled climate chamber, enabling a comprehensive characterization process of the humidity transfer into the IGBT module. The proposed method allows for a detailed study of the moisture distribution as well as the effect of the temperature on the moisture within an IGBT module. We leverage the advanced capabilities of commercial finite element software to complement our experimental findings. These simulations enable a deeper understanding of the moisture distribution's symmetries and provide invaluable insights into simplifying the complex simulations. By integrating these diverse methodologies, we develop a comprehensive approach that deciphers the spatial distribution of humidity within the module and its real-time responses to environmental conditions. This integrated approach holds an immense potential for analyzing optimal system performance and facilitating self-optimization of the inverter by predicting stress induced by humidity.
{"title":"Investigating Humidity Transfer in IGBT Modules: An Integrated Experimental and Simulation Approach","authors":"Valeriya Titova;Martin Lapke","doi":"10.1109/OJPEL.2024.3389105","DOIUrl":"10.1109/OJPEL.2024.3389105","url":null,"abstract":"This study presents a novel, integrated approach to investigating and characterizing the impact of humidity on Insulated-Gate Bipolar Transistors (IGBTs) within large-scale inverter systems. Combining meticulously designed experimental setups and advanced finite element simulations, we delve deep into the complex dynamics of moisture transfer within IGBT modules. Our research demonstrates a meticulously designed experimental setup within a controlled climate chamber, enabling a comprehensive characterization process of the humidity transfer into the IGBT module. The proposed method allows for a detailed study of the moisture distribution as well as the effect of the temperature on the moisture within an IGBT module. We leverage the advanced capabilities of commercial finite element software to complement our experimental findings. These simulations enable a deeper understanding of the moisture distribution's symmetries and provide invaluable insights into simplifying the complex simulations. By integrating these diverse methodologies, we develop a comprehensive approach that deciphers the spatial distribution of humidity within the module and its real-time responses to environmental conditions. This integrated approach holds an immense potential for analyzing optimal system performance and facilitating self-optimization of the inverter by predicting stress induced by humidity.","PeriodicalId":93182,"journal":{"name":"IEEE open journal of power electronics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10502161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a novel scheme for condition monitoring of dc-link capacitors in modular multilevel converters (MMCs). The proposed solution uses estimated capacitance values of the dc-link capacitors for indicating their state-of-health (SoH). Moreover, a comparative approach is proposed, where the estimated capacitances of all submodule capacitors are used to separate parameter drifts caused by aging from parameter drifts caused by other factors such as temperature change. It is shown in simulation and experimental results that an equal drift in all capacitance estimates can be a result of factors other than aging. However, a drift in the capacitance of one capacitor compared to the average capacitance of all submodules may be attributed to aging of that specific unit. Using the proposed comparative technique, there is no need for additional temperature sensors to account for the effect of temperature variations on the online estimations. Simulation and experimental results demonstrate an overall estimation error of less than 1% when applying the proposed comparative technique.
{"title":"Practical Online Condition Monitoring of DC-Link Capacitors in Modular Multilevel Converters: A Comparative Approach","authors":"Mohsen Asoodar;Mehrdad Nahalparvari;Christer Danielsson;Hans-Peter Nee","doi":"10.1109/OJPEL.2024.3387829","DOIUrl":"10.1109/OJPEL.2024.3387829","url":null,"abstract":"This article presents a novel scheme for condition monitoring of dc-link capacitors in modular multilevel converters (MMCs). The proposed solution uses estimated capacitance values of the dc-link capacitors for indicating their state-of-health (SoH). Moreover, a comparative approach is proposed, where the estimated capacitances of all submodule capacitors are used to separate parameter drifts caused by aging from parameter drifts caused by other factors such as temperature change. It is shown in simulation and experimental results that an equal drift in all capacitance estimates can be a result of factors other than aging. However, a drift in the capacitance of one capacitor compared to the average capacitance of all submodules may be attributed to aging of that specific unit. Using the proposed comparative technique, there is no need for additional temperature sensors to account for the effect of temperature variations on the online estimations. Simulation and experimental results demonstrate an overall estimation error of less than 1% when applying the proposed comparative technique.","PeriodicalId":93182,"journal":{"name":"IEEE open journal of power electronics","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496921","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}