Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain Using Homogeneous Graph Neural Networks

Khamis, Ahmed K.; Agamy, Mohammed

doi:10.1109/ojcas.2023.3234244

Cited by 10 publications

(4 citation statements)

References 56 publications

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“…Notably, these examples do not necessarily consider the circuit and graph reversibility because the output consists of images of the component layout [16] and numerical values for the simulation [13]. A method similar to the one-hot embedding vector concatenates the real values of the circuit constants into a one-hot vector [25] [26] [27]. However, these methods are not suitable for GNNs because they are not orthogonal, whereas the one-hot embedding vector is orthogonal between the circuit components.…”

Section: A Previous Workmentioning

confidence: 99%

Circuit2Graph: Circuits With Graph Neural Networks

Yamakaji,

Shouno,

Fukushima

2024

IEEE Access

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Circuit design requires trial and error in both prototyping and simulation owing to the high degrees of freedom and mutual interference between the components. In this study, we propose a novel approach to address this challenge by introducing a transformation method that converts circuits into graph networks. This transformation is achieved with no loss of information, and we evaluate its accuracy through the application of graph classification by utilizing graph neural networks. We assume that the information degradation can result from self-loops, multi-edges, and heterogeneous graphs. To mitigate these issues, we propose a method that effectively reduces their impact. The results of this study demonstrate the effectiveness of our proposed method, as it achieves an accuracy of 97.89%. This represents a significant improvement of 5.2% when compared with the conventional method. Notably, our proposed method is applicable to generalpurpose circuits. This makes it a valuable addition to the existing repertoire of circuit solution methods, alongside analytical and simulation approaches.

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Section: A Previous Workmentioning

confidence: 99%

Circuit2Graph: Circuits With Graph Neural Networks

Yamakaji,

Shouno,

Fukushima

2024

IEEE Access

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“…(i) Converter-level research. To better implement machine learning methods for tasks including regression, classification, clustering, and synthesis of power electronic converter circuits, a systematic circuit mapping framework is proposed in [68]. In this work, the bond graph is used for converter modeling to cope with the multi-physics nature of power converters, and GCN with MEAN pooling is used for circuit feature encoding, such that, datasets can be generated in a unique graphical format for downstream tasks, e.g., classification of converter types.…”

Section: B Existing Applications Of Gnns In Power Electronicsmentioning

confidence: 99%

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Li,

Xue,

Zargari

et al. 2023

IEEE Access

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Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graphstructured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs' general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNNbased case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs' unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs' latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

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“…Ref. [6] proposes a graph representation to model the converter for CCM and DCM operation for Large signal average modeling is proposed using a neural network in [5]. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [6] proposes a graph representation to model the converter for CCM and DCM operation for using machine learning. A reduced order method to analyze the DCM buck converter is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

LCL Trap Filter Analysis with a PFC Isolated Ćuk Converter Using SiC MOSFET for DCM

Şehirli

2024

Energies

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The main contribution of the paper concerns the use of an LCL trap filter with a PFC isolated Ćuk converter. Further, SiC MOSFET is used with a PFC isolated Ćuk converter designed for 50 W with 42 kHz in DCM. A small-signal model of the converter is cascaded with the filter model to investigate the effect of the filter on the whole system. Moreover, large-signal and small-signal models of the converter are compared to investigate the requirement of the small-signal analysis. In addition, an LTspice simulation using SiC MOSFET of the system is conducted and the results are compared by the applications for both LC and LCL trap filters with respect to different loading conditions. Further, the LCL trap filter is compared with the LC filter regarding the PF, THD, and efficiency. Controller design considering the filter is also presented. In addition, the converter is operated and compared using linear and nonlinear loads for each filter. Parametric variation in the filter components is investigated. As a result of the simulation and applications, the THD of the grid current is 4.83% and the PF is 0.998, meeting the standards, and the overall efficiency of the system is 85% with the LCL trap filter. It can be concluded that the presented filter provides better results than the LC filter.

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Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain Using Homogeneous Graph Neural Networks

Cited by 10 publications

References 56 publications

Circuit2Graph: Circuits With Graph Neural Networks

Circuit2Graph: Circuits With Graph Neural Networks

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

LCL Trap Filter Analysis with a PFC Isolated Ćuk Converter Using SiC MOSFET for DCM

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