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Due to their significant performance, Phase-Shifted Full-Bridge Converters (PSFBCs) have gained widespread adoption in medium- and high-power applications. The performance of a PSFBC is greatly influenced by its magnetic components, namely the transformer and resonance inductor. To address these challenges, this paper proposes a parameter optimization of magnetic components for PSFBCs, specifically the transformer turns ratio and resonance inductor value, to enhance conversion efficiency and minimize operational loss. A digital twin of PSFBCs enabling a more accurate loss estimation is proposed to achieve this objective. The proposed loss estimation method precisely calculates the effective and circulation intervals and the corresponding current points of the primary-side transformer current, resulting in improved accuracy. By leveraging the digital twin, the effects of transformer turns ratio and resonant inductor value on the conversion efficiency of a PSFBC can be efficiently simulated. This facilitates the parameter optimization of magnetic components, thereby minimizing operational losses across different application scenarios. This paper also designs and implements a PSFBC prototype with a rated input voltage of 380 V, output voltage of 24 V, and output current of 20 A. The experimental results show the influences of transformer turns ratio and resonant inductor value on the PSFBC and validate the proposed digital twin. The proposed parameter optimization of magnetic components is further evaluated across two application scenarios with varying utilization rates. The simulation results indicate a reduction of approximately 14% in operational loss per hour after applying the parameter optimization of magnetic components for the PSFBC used as a battery charger. The results demonstrate the effectiveness and practicality of the proposed digital twin in designing PSFBCs. [ABSTRACT FROM AUTHOR]

The valve‐side bushing of the UHV converter transformer is the key equipment in the DC transmission project, and its running state directly affects the security and stability of the power system. This paper analyses the attributes of the physical entities, uses the digital twins to establish the state feature set and proposes a zero‐sample state evaluation algorithm for the valve‐side bushing. First, the geometric, materials and electrical characteristics are analyzed, and the detailed components of carrier current are obtained by empirical mode decomposition. Then, COMSOL is used to establish digital twins, verify the validity of twins with axial heat distribution of bushing, and establish a state feature set with the extreme temperature inside and outside bushing. Finally, the fuzzy clustering algorithm is used to classify the state feature set, and the similarity is used as the index to realize the zero‐sample state evaluation of the valve‐side bushing. Through the demonstration and analysis of examples, the evaluation model solves the problems of difficulty in extracting the internal features, fewer fault samples, and training difficulty, which is conducive to improving the operation and maintenance management level of power transmission equipment. [ABSTRACT FROM AUTHOR]

Transformer is a key equipment for constructing power system, and its safe and stable operation is the basis of power system. In order to solve the problems of difficult identification and low detection accuracy of transformer winding at operating state, in this paper, a diagnosis method based on digital twinning technique for transformer winding failure is proposed. Firstly a digital twin transformer model with high simulation degree based on transformer entities was built, and then multiple physical field simulation were used to deduce the information change of oil tank surface vibration information of digital twin transformer under different working conditions, various winding failures. Here, the vibration signal on oil tank is effectively decomposed to extract the feature vector by independent component analysis method and wavelet packet transform, which in turn is based on neural network algorithm to learn and diagnose the failure signal of winding. In order to test the accuracy and feasibility of the method, the diagnostic test of oil tank vibration signal detection of health and fault transformer is carried out on an experimental prototype of transformer with 110 kV, and the diagnostic results show that this method can diagnose the problems occurred in transformer windings with high efficiency and accuracy. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]