Physical Modality Prediction for Unseen Power Electronic Devices
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Solution Overview
Problem
Existing methods for modeling power electronic devices face challenges in accurately predicting physical modalities across diverse architectures, topologies, and configurations due to reliance on device-specific datasets, labor-intensive calibration, and inability to handle real-world data imperfections such as noise and missing values, limiting scalability and adaptability.
Innovation Solution
A unified machine learning framework using self-supervised learning and few-shot learning techniques to train a generalized model that can predict physical modalities of unseen devices, with mechanisms to handle data imperfections and adapt to new devices with limited labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device-specific datasets and labor-intensive calibration methods are used, then modeling accuracy for specific power electronic devices is improved, but scalability and adaptability to diverse architectures deteriorate
Solution Approach 1:
The patent applies universality by training a single machine learning model on a diverse dataset encompassing multiple power electronic device architectures, topologies, and configurations. This unified model can predict physical modalities across different device types without requiring separate device-specific models, thereby achieving both accuracy and scalability. The model learns common patterns and relationships that generalize across diverse devices, eliminating the need for labor-intensive calibration for each specific device type.
2Measurement precision
If comprehensive device-specific training data is collected, then prediction accuracy for known devices is improved, but data collection burden and time requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training a universal model on a comprehensive dataset that covers diverse power electronic device architectures, topologies, and configurations. This pre-trained model captures general patterns and relationships that are transferable to new devices. When a new device needs to be modeled, the system can quickly adapt using few-shot learning with minimal additional data, rather than collecting extensive device-specific data from scratch. This approach significantly reduces the data collection burden and time requirements for new devices while maintaining high prediction accuracy.
3Loss of information
If traditional modeling methods are used, then detailed device-specific insights are obtained, but ability to handle noise and missing values deteriorates
Solution Approach 1:
The patent applies self-service by designing a machine learning model that automatically handles noise and missing values in the input data without requiring manual preprocessing or cleaning. The model incorporates robustness mechanisms that allow it to learn from imperfect real-world data directly, extracting meaningful patterns even when the data contains noise or gaps. This self-service capability maintains the ability to obtain detailed device-specific insights while being inherently robust to data imperfections, eliminating the need for labor-intensive data cleaning processes.
Data Source
AI summary
Power electronic device prediction systems and methods for using power electronic device models to predict the physical modalities of unknown power electronic devices. These unknown power electronic devices have not been seen previously by the power electronic device models. For example, if the class of power electronic devices is power converters, then a power converter model is trained on known physical modalities from different power converters and the model is used to predict an unknown physical modality of a power converter that the model has not seen before. In some examples, the training is unsupervised, such that the training data is unlabeled. In other examples, the training uses self-supervised techniques using unlabeled training data and then the model is refined using few-shot learning techniques and a small amount of labeled training data. This allows the models to quickly adapt to predict the physical modalities of unknown power electronic devices.