Yazar "Kuwil, Farag Hamed" seçeneğine göre listele

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    Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm
    (Mdpi, 2023) Alsharif, Abdulgader; Tan, Chee Wei; Ayop, Razman; Al Smin, Ahmed; Ali Ahmed, Abdussalam; Kuwil, Farag Hamed; Khaleel, Mohamed Mohamed
    The area of a Microgrid (mu G) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.
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    A novel data clustering algorithm based on gravity center methodology
    (Pergamon-Elsevier Science Ltd, 2020) Kuwil, Farag Hamed; Atila, Umit; Abu-Issa, Radwan; Murtagh, Fionn
    The concept of clustering is to separate clusters based on the similarity which is greater within cluster than among clusters. The similarity consists of two principles, namely, connectivity and cohesion. However, in partitional clustering, while some algorithms such as K-means and K-medians divides the dataset points according to the first principle (connectivity) based on centroid clusters without any regard to the second principle (cohesion), some others like K-medoids partially consider cohesion in addition to connectivity. This prevents to discover clusters with convex shape and results are affected negatively by outliers. In this paper a new Gravity Center Clustering (GCC) algorithm is proposed which depends on critical distance (lambda) to define threshold among clusters. The algorithm falls under partition clustering and is based on gravity center which is a point within cluster that verifies both the connectivity and cohesion in determining the similarity of each point in the dataset. Therefore, the proposed algorithm deals with any shape of data better than K-means, K-medians and K-medoids. Furthermore, GCC algorithm does not need any parameters beforehand to perform clustering but can help user improving the control over clustering results and deal with overlapping and outliers providing two coefficients and an indicator. In this study, 22 experiments are conducted using different types of synthetic, and real healthcare datasets. The results show that the proposed algorithm satisfies the concept of clustering and provides great flexibility to get the optimal solution especially since clustering is considered as an optimization problem. (C) 2020 Elsevier Ltd. All rights reserved.
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    A novel hybridization approach to improve the critical distance clustering algorithm: Balancing speed and quality
    (Pergamon-Elsevier Science Ltd, 2024) Kuwil, Farag Hamed; Atila, Uemit
    Clustering is a prominent research area, with numerous studies and the development of hundreds of algorithms over the years. However, a fundamental challenge in clustering research is the trade-off between algorithm speed and clustering quality. Existing algorithms tend to prioritize either fast execution with compromised clustering quality or slower performance with superior clustering results. In this study, we propose a novel CDC-2 algorithm, an improved version of the Critical Distance Clustering (CDC) algorithm, to address this challenge. Inspired by the concepts of hybridization in biology and the division of labor in the economic system, we present a new hybridization strategy. Our approach integrates the connectivity and coherence aspects of the K-means and CDC-2 algorithms, respectively, allowing us to combine speed and quality in a single algorithm. This approach is referred to as the CDC++ algorithm, and it is characterized as a hybrid that combines elements from two algorithms, K-means and CDC-2, in order to leverage their strengths while mitigating their weaknesses. Moreover, the structure and mechanism of the CDC++ algorithm led to the introduction of a new concept called object autoencoder. Unlike traditional feature reduction methods, this concept focuses on object reduction, representing a significant advancement in clustering techniques. To validate our approach, we conducted experimental studies on thirteen synthetic and five real datasets. Comparative analysis with four well-known algorithms demonstrates that our proposed development and hybridization enable efficient processing of largescale and high-dimensional datasets without compromising clustering quality.