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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Mohd, Izzeldin I.
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A neural network controller and a simple circuit of SVPWM technique to increase five-level VSC STATCOM performance during voltage sag and swell Almelian, Mohamad Milood; Mohd, Izzeldin I.; Aker, Elhadi. E.; Omran, Mohamed A.; Salem, Mohamed; Ahmad, Abu Zaharin; Albishti, Abibaker A.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1478-1489

Abstract

The most critical disturbance faced in the electrical distribution systems is power service interruptions due to voltage sag or swell which results in economic losses on the user’s side. To compensate voltage sag or swell, advanced custom power devices are used and one of such devices is the static synchronous compensation (STATCOM). This paper presents the implementation of 5-level voltage source converter (VSC) STATCOM using a neural network (NN) and a simplified space vector pulse width modulation (SVPWM) circuit. The primary objective of the NN controller and SVPWM circuit is to enhance the performance and response time of the STATCOM system, specifically in terms of improving voltage and power factor (PF) when faced with voltage sag or swell. The performance of STATCOM was examined within the context of the IEEE 3-bus system. The investigation focused on two scenarios: a single-line-to-ground fault resulting in voltage sag, and the sudden connection of a capacitive load leading to voltage swell. The findings unequivocally demonstrated the efficacy of the STATCOM with a NN controller in comparison to a conventional controller. The utilization of the NN controller resulted in notable improvements in voltage and PF within a remarkably short time frame of 0.02 seconds.
Analyzing the instructions vulnerability of dense convolutional network on GPUS Adam, Khalid; Mohd, Izzeldin I.; Ibrahim, Younis
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4481-4488

Abstract

Recently, deep neural networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the graphics processing units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy.
Co-Authors Adam, Khalid Ahmad, Abu Zaharin Aker, Elhadi. E. Albishti, Abibaker A. Almelian, Mohamad Milood Ibrahim, Younis Omran, Mohamed A. Salem, Mohamed