Design Optimization of a Three-Phase Transformer Using Finite Element Analysis
Optimization of design parameters of a transformer is a crucial task to increase efficiency and lower the material cost. This research presents an approach to model a three-phase transformer and optimize design parameters to minimize the volume and loss. ANSYS Maxwell 2D is used to model the transformer and analyze it for different design parameters. The multi-objective differential evolution algorithm is used to find optimum design parameters that minimize the volume and loss. In this paper, we present the optimum design parameters for a 1 kVA transformer with a particular input and output voltage specification. The transformer with these optimum design parameters is then tested for different loading conditions and power factor values. The results show that the maximum efficiency is obtained for 75% loading condition with unity power factor. As the power factor decreases, the efficiency decreases as well.
Code (0)
등록된 구현이 없습니다.
Tasks
UnitySimilar Papers 제목 키워드 기반
Topology Optimization in Three-Phase C-Core SRMs
This paper investigates the optimized combination of rotor and stator teeth in a three-phase switched reluctance motor featuring a connected C-core topology to attain a larger winding area and, thus, a higher electrical …
Minimum Phase Linear Antenna Array Design
The paper considers the design of minimum phase discrete linear arrays. The paper introduces recent advances for the design of minimum phase Finite Impulse Response filters, as applied to the design of minimum phase line…
Phase inpainting in time-frequency plane
We propose a new problem of missing data reconstruction in the time-frequency plane. This problem called phase inpainting, consists in reconstructing a signal from time-frequency observations where all amplitudes and som…
JCDNet: Joint of Common and Definite phases Network for Weakly Supervised Temporal Action Localization
Weakly-supervised temporal action localization aims to localize action instances in untrimmed videos with only video-level supervision. We witness that different actions record common phases, e.g., the run-up in the High…
Action LocalizationMultiple Instance LearningTemporal Action LocalizationWeakly-supervised Learning+1Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width
Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs with infinite width (Luo et al., 2021),…