arXiv subjects

Ahmed Altaher

Publications and source records attributed to Ahmed Altaher.

3 recordsLinked to original sources

arXiv2026

Optimum Battery Depth of Discharge of Stand-alone Hybrid System Using the MOPSO Method

This paper presents an optimized design of a Standalone Solar PV/Battery (SSPVB) system to address energy reliability and cost efficiency challenges in off-grid environments. The proposed system integrates a Multi-Objective Particle Swarm Optimization (MOPSO) approach and validates the results using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The optimization process aims to minimize both the Cost of Energy (COE) and Loss of Load Probability (LLP), while examining the effects of Battery Depth of Discharge (DOD) on system reliability and lifecycle cost. Results indicate that an optimal DOD of approximately 70% yields a COE of 0.2059 USD/kWh with zero LLP, demonstrating strong reliability and cost-effectiveness. Comparative analysis shows that both MOPSO and NSGA-II methods achieve consistent outcomes, with MOPSO exhibiting faster convergence. The study provides valuable insights into optimal battery sizing for stand-alone systems, contributing to modern optimization practices in renewable energy applications.

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arXiv2022

COVID-19 Classification Using Deep Learning Two-Stage Approach

In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.

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arXiv2015

Evaluation of Time-Critical Communications for IEC 61850-Substation Network Architecture

Present-day developments, in electrical power transmission and distribution, require considerations of the status quo. In other meaning, international regulations enforce increasing of reliability and reducing of environment impact, correspondingly they motivate developing of dependable systems. Power grids especially intelligent (smart grids) ones become industrial solutions that follow standardized development. The International standardization, in the field of power transmission and distribution, improve technology influences. The rise of dedicated standards for SAS (Substation Automation Systems) communications, such as the leading International Electro-technical Commission standard IEC 61850, enforces modern technological trends in this field. Within this standard, a constraint of low ETE (End-to-End) latency should be respected, and time-critical status transmission must be achieved. This experimental study emphasis on IEC 61850 SAS communication standard, e.g. IEC 61850 GOOSE (Generic Object Oriented Substation Events), to implement an investigational method to determine the protection communication delay. This method observes GOOSE behaviour by adopting monitoring and analysis capabilities. It is observed by using network test equipment, i.e. SPAN (Switch Port Analyser) and TAP (Test Access Point) devices, with on-the-shelf available hardware and software solutions.

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