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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Improved hybridization of evolutionary algorithms with a sensitivity-based decision-making technique for the optimal planning of shunt capacitors in radial distribution systems
Applied Sciences (Switzerland), Volume 10, No. 4, Article 1384, Year 2020
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Description
In this paper, an improved hybridization of an evolutionary algorithm, named permutated oppositional differential evolution sine cosine algorithm (PODESCA) and also a sensitivity-based decision-making technique (SBDMT) are proposed to tackle the optimal planning of shunt capacitors (OPSC) problem in different-scale radial distribution systems (RDSs). The evolved PODESCA uniquely utilizes the mechanisms of differential evolution (DE) and an enhanced sine-cosine algorithm (SCA) to constitute the algorithm's main structure. In addition, quasi-oppositional technique (QOT) is applied at the initialization stage to generate the initial population, and also inside the main loop. PODESCA is implemented to solve the OPSC problem, where the objective is to minimize the system's total cost with the presence of capacitors subject to different operational constraints. Moreover, SBDMT is developed by using a multi-criteria decision-making (MCDM) approach; namely the technique for the order of preference by similarity to ideal solution (TOPSIS). By applying this approach, four sensitivity-based indices (SBIs) are set as inputs of TOPSIS, whereas the output is the highest potential buses for SC placement. Consequently, the OPSC problem's search space is extensively and effectively reduced. Hence, based on the reduced search space, PODESCA is reimplemented on the OPSC problem, and the obtained results with and without reducing the search space by the proposed SBDMT are then compared. For further validation of the proposed methods, three RDSs are used, and then the results are compared with different methods from the literature. The performed comparisons demonstrate that the proposed methods overcome several previous methods and they are recommended as effective and robust techniques for solving the OPSC problem. © 2020 by the author.
Authors & Co-Authors
Alkayem, Nizar Faisal
China, Nanjing
Hohai University
Haes Alhelou, Hassan Haes
Syrian Arab Republic, Latakia
Université Tichrine
Siano, Pierluigi
Italy, Salerno
Università Degli Studi Di Salerno
Parente, Mimmo
Italy, Salerno
Università Degli Studi Di Salerno
Statistics
Citations: 7
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.3390/app10041384
ISSN:
20763417
Research Areas
Genetics And Genomics
Study Design
Cross Sectional Study