Welcome to ELEC 2023

6th International Conference on Electrical Engineering (ELEC 2023)

January 21-22, 2023, Virtual Conference

Accepted Papers
Solving Power Quality Issues by Hybrid Distribution Transformer

Fajer Alelaj, Mohamed Dahidah and Haris Patsios, Department of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, UK


Hybrid transformers (HT) have the advantages of the conventional transformer, the regulatory abilities of power electronic converters, and reduce the impact of the grid. The impacts of the existing grid are voltage sag, voltage swell, harmonic distortion, and voltage unbalanced. The power electronic converter has a controllable advantage such as regulating the voltage and can transfer only a fraction of the power. In this paper, the proposed back-to-back converter included an active front rectifier and a modular multilevel converter (MMC) was simulated by MATLAB/Simulink software. The proposed backto-back converter was used at the primary side of the distribution transformer to compensate for the voltage sag and swell issues. The simulation results were reported at different conditions such as various supply voltages and various loads to ensure the proposed system can regulate the output voltage.


Hybrid transformer, voltage sag, voltage swell, back-to-back converter.

Scaled Quantization for the Vision Transformer

Yangyang Chang and Gerald E. Sobelman, Department of Electrical Engineering and Computer Science, University of Minnesota, Minneapolis, MN, USA


Quantization using a small number of bits shows promise for reducing latency and memory usage in deep neural networks. However, most quantization methods cannot readily handle complicated functions such as exponential and square root, and prior approaches involve complex training processes that must interact with floating-point values. This paper proposes a robust method for the full integer quantization of vision transformer networks without requiring any intermediate floating-point computations. The quantization techniques can be applied in various hardware or software implementations, including processor/memory architectures and FPGAs.


Neural networks, Quantization, Vision transformer.