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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Advances in Applied Sciences
Sabah A. Gitaffa
University of Technology
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Influence of end-effect and end-winding on the electromagnetic losses and efficiency in high speed permanent magnet machines Ahlam Luaibi Shuraiji; Sabah A. Gitaffa; Kassim Rasheed Hameed; Salam Waley Shneen
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i4.pp2033-2040

Abstract

Permanent-magnet excitation machines (SPMMs) having mounted magnet on the outer surface of their rotor are preferred for high speed applications such as turbochargers, mechanical turbo-compounding systems, racing engines and fuel pumps, over other types of machines including induction and switched-reluctance machines, since the SPMMs integrate the features of high torque density, compact rotor structure, high reliability and simple structure. However, in the SPMMs, due to the need for a retaining sleeve for the rotor, a large magnetic airgap results and consequently a large magnet thickness is required, hence the magnetic end-effect is relatively high. On the other hand, the use of an overlapping distributed winding leads to a significantly large end-winding length. Hence, the end-effect and the end-winding influences on the performances of a high-speed SPMM is considered in this paper. With a view to get the impact of the end-effect, a comparison between three-dimensional (3D-FEA) results and counterparts two-dimensional finite element analyses (2D-FEA) have been conducted. Results show that, higher efficiency at low torque and low speed due to the low electromagnetic losses and at high speeds due to the high flux-weakening capability are seen when the influences of end-effect as well as end-winding are taken into account.
Increasing the efficiency of deep learning performance using adaptive filters Suad Khairi Mohammed; Sabah A. Gitaffa; Reem I. Dawai
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp775-789

Abstract

Deep learning algorithms have become one of the most important innovative technologies that have entered almost all areas of life. These technologies perform complex operations and deal with huge data sets. One of the benefits of deep learning is the inherent flexibility in developing approximate estimates for vast and diverse data sets. Data scientists can develop approximate estimates of almost anything using deep learning and neural networks. The main challenges in deep learning include the problem of data quality and quantity while ensuring large, diverse, and high-quality datasets. It also suffers from the problem of providing computational resources due to the high demand for powerful devices, such as processing and memory units. Additionally, it suffers from the problem of interpretability of the case due to difficulty in understanding and explaining typical decisions. This study proposes an innovative method to reduce these problems in the working mechanisms of deep learning algorithms by merging their layers and hybridizing them using adaptive digital filters. These filters help provide devices for efficient resources and memory units, in addition to the capabilities of analyzing and interpreting various states of processing unit availability. In this study, models of hybrid deep learning techniques with adaptive digital filters were designed and implemented, obtaining good results in reducing training error rates, improving the efficiency of outputs, and reducing the computational effort to high levels.
Co-Authors Ahlam Luaibi Shuraiji Kassim Rasheed Hameed Reem I. Dawai Salam Waley Shneen Suad Khairi Mohammed