An Online Digital Twin based Health Monitoring Method for Boost Converter using Neural Network
Yizhou Lu, Mengfan Zhang, Lars Nordström, Qianwen Xu
Abstract
This paper proposes a neural network-based digital twin for online health monitoring of vulnerable components in converters. The proposed digital twin consists of a physics-informed model with uncertain parameters, and a neural network (NN) for real-time model updating and health monitoring of components. This method is noninvasive, without extra circuits, and can identify parameters in real-time with high efficiency. Simulation and experiment are conducted to validate the effectiveness of the proposed method in accurate parameter identification and degradation monitoring of capacitor and MOSFET.
Topics & Concepts
ConvertersArtificial neural networkComputer scienceCapacitorIdentification (biology)Artificial intelligenceElectronic engineeringMachine learningData miningEngineeringVoltageElectrical engineeringBotanyBiologySilicon Carbide Semiconductor TechnologiesIntegrated Circuits and Semiconductor Failure AnalysisAdvancements in Semiconductor Devices and Circuit Design