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RISS 인기검색어
- 검색키워드
- 저자 : El-Sehiemy Ragab A. (검색결과 2 건)
- 무료
- 기관 내 무료
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Abou El-Ela Adel A.,El-Sehiemy Ragab A.,Shaheen Abdullah M.,Shalaby Ayman S. 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.6
As the wind speed is intermittent and unpredictable, statistical distribution approaches have been used to describe wind dates. The Weibull distribution with two parameters is thought to be the most accurate way for modeling wind data. This study seeks wind energy assessment via searching for optimal parameter estimation of the Weibull distribution. For this target, several analytical and heuristic methods are investigated. The analytical methods such as maximum likelihood method, moment method, energy pattern factor method (EPFM), and empirical method (EM) are used to find these optimal parameters. Also, these parameters are obtained by four heuristic optimization algorithms called particle swarm, crow search, aquila optimizer, and bald eagle search optimizers. The simulation results of analytical and heuristics are assessed together to identify the best probability density function (PDF) of wind data. In addition, these competitive models are submitted to find the most appropriate model to represent wind energy production. In all methods, the error between actual and estimated wind energy density is computed as the target fitness function. The simulation tests are carried out based on per year real data that are collected from Zafaranah and Shark El-Ouinate sites in Egypt. Also, different indicators of fitness properties are assessed such as the root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and wind production deviation (WPD). The simulation results declare that the proposed bald eagle search optimization algorithm offers greater accuracy than other analytical and heuristic algorithms in estimating the Weibull parameters. Besides, statistical analysis of the compared methods demonstrates the high stability of the BES algorithm. Moreover, the BES algorithm presents the fastest convergence compared to the others. Furthermore, different models are analyzed to deduce the nonlinear relationship between the wind output power and the regarding speed where the error of wind energy density between actual and estimated is greatly minimized using the cubic model at least values of statistical indicators.
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Enhanced Real Coded Genetic Algorithm for Optimal DG Placement in a Radial Distribution System
Almabsout Emad Ali,El-Sehiemy Ragab A.,Bayoumi Ahmed Saeed Abdelrazek 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4
Active and reactive power flows in distribution systems have a significant impact on the efficiency of power distribution systems. The optimal placement of distributed power generation units is the most popular approach for improving power quality, voltage profile and reducing the total power loss of distribution systems. This paper presents an enhanced algorithm to handle the optimal capacity and placement of multi-distributed generation units in radial distribution networks (RDN). In this light, a proposed local search scheme has been integrated into the real coded genetic algorithm by the proposed algorithm to exploit its advantages reduce the search time required for finding the optimal placement and size of DG in RDN. The proposed genetic algorithm evaluates population solutions by minimizing total real power losses in a system and improve the voltage profile. To investigate the performance of the proposed algorithm, two common radial systems have been used, namely, IEEE 33-bus, 69-bus networks. The experimental results demonstrate that the implemented enhanced genetic algorithm can find the best solutions to the problem effectively reducing the power loss and improving the voltage profile and outperform other current literature algorithms. Moreover, the energy losses cost, and total voltage deviation have been investigated. Significant improvement in the power losses and voltage profile enhancement with the increasing of distributed generation units has been reached. The feasibility of the method for practical application has been investigated and validated with preserving the constraints and operational restrictions.
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