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Enhancing Efficiency in Distribution Power Networks by Power Factor Controlling of Renewable Energy Generators: a Case Study of Iraqi Wasit Network
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Integrating Renewable Energy (RE) into Distribution Power Networks (DPNs) is a choice for efficient and sustainable electricity. Controlling the power factor of these sources is one of the techniques employed to manage the power loss of the grid. Capacitor banks have been employed to control phantom power, improving voltage and reducing power losses for several decades. The voltage sag and the significant power losses in the Iraqi DPN make it good evidence to be a case study proving the efficiency enhancement by adjusting the RE power factor. Therefore, this paper studies a part of the Iraqi network in a windy and sunny region, the Badra-Zurbatya-11 kV feeder, in the Wasit governorate. A substation of hybrid RE sources is connected to this feeder to compensate for the low voltage and high power losses within three different electricity demand conditions: low, medium, and high. The results are compared with a case when an optimum capacitor placement is applied. By using the CYME 9 software for analysis, the research shows the benefits of adding RE to the grid. The results have approved the hybrid RE penetration benefits as the power losses are reduced in low, medium, and high scenarios by 36.26%, 43%, and 48.4%, respectively, besides the voltage sag elimination.

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Publication Date
Wed Feb 20 2019
Journal Name
Political Sciences Journal
the circulation of power in Iraq after 2003
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The deliberation and exchanged of power is a matter of political importance For the Iraqi political system after 2003, The deliberation of power and its democratic transition and electoral integrity Is a good indicator of political stability and progress In the Iraqi parliamentary political system , The transfer of authority in terms of adopting the deliberative approach represents a structural and institutional dimension of the political process , it led the power to become Authority of the Foundation Exercised in accordance with the Constitution and the law not real power , And lead to the recognition of the existence of an official parliamentary opposition and its right to take political power later through the ballot box Makes the pr

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Publication Date
Tue Mar 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
The Bayesian Estimation for The Shape Parameter of The Power Function Distribution (PFD-I) to Use Hyper Prior Functions
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The objective of this study is to examine the properties of Bayes estimators of the shape parameter of the Power Function Distribution (PFD-I), by using two different prior distributions for the parameter θ and different loss functions that were compared with the maximum likelihood estimators. In many practical applications, we may have two different prior information about the prior distribution for the shape parameter of the Power Function Distribution, which influences the parameter estimation. So, we used two different kinds of conjugate priors of shape parameter θ of the <

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Publication Date
Mon Feb 01 2016
Journal Name
Journal Of Civil Engineering Research
Distribution Factor of Curved I-Girder Bridges under Iraqi Standard Bridge Live Loads
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Publication Date
Sun Nov 01 2020
Journal Name
International Journal Of Engineering
Pavement Maintenance Management Using Multi-objective Optimization: (Case Study: Wasit Governorate-Iraq)
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Publication Date
Thu Oct 01 2020
Journal Name
Ieee Transactions On Very Large Scale Integration (vlsi) Systems
Low-Power, Highly Reliable Dynamic Thermal Management by Exploiting Approximate Computing
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With the continuous downscaling of semiconductor processes, the growing power density and thermal issues in multicore processors become more and more challenging, thus reliable dynamic thermal management (DTM) is required to prevent severe challenges in system performance. The accuracy of the thermal profile, delivered to the DTM manager, plays a critical role in the efficiency and reliability of DTM, different sources of noise and variations in deep submicron (DSM) technologies severely affecting the thermal data that can lead to significant degradation of DTM performance. In this article, we propose a novel fault-tolerance scheme exploiting approximate computing to mitigate the DSM effects on DTM efficiency. Approximate computing in hardw

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Publication Date
Fri Jan 01 2016
Journal Name
Proceedings Of The Eleventh International Network Conference (inc 2016)
A review on power consumption reduction techniques on OFDM
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Publication Date
Mon Oct 01 2018
Journal Name
Arpn Journal Of Engineering And Applied Sciences
Achieving a theoretical approximation characterize the stopping power of heavy ion in D-T plasma
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The dependence of the energy losses or the stopping power for the ion contribution in D- T hot plasma fuels upon the corresponding energies and the related penetrating factorare arrive by using by a theoretical approximation models. In this work we reach a compatible agreement between our results and the corresponding experimental results.

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Publication Date
Wed Aug 30 2023
Journal Name
Mathematical Modelling Of Engineering Problems
Dynamic Low Power Clustering Strategy in MWSN
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Publication Date
Wed Jan 01 2025
Journal Name
The International Journal Of Communication And Linguistic Studies
Power and Identity in American Diplomatic Discourse
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Publication Date
Fri Apr 24 2026
Journal Name
F1000research
Machine Learning Assisted Hybrid Cuckoo Search for Predictive Optimization in Renewable Energy Systems
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Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

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