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Optimal power flow using the AMTPG-Jaya algorithm
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DOI：10.1016/j.asoc.2020.106252.png)
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This work proposes the implementation of a recently invented meta-heuristic optimization solver namely, an adaptive multiple teams perturbation-guiding Jaya (AMTPG-Jaya) technique to tackle with diverse single goal optimum power flow (OPF) forms. The AMTPG-Jaya solver employs numerous populations named as teams to investigate the search domain. Each team is guided by a number of movement equations (exploration pathways). The algorithm adjusts the number of teams along with the approaching to the finest so-far nominee solution. In this study, an original AMTPG-Jaya inspired approach to handle the OPF formulation is suggested. The efficacy of the AMTPG-Jaya solver is scrutinized and tested on two well-known standard power systems with different goal functions. The optimization outcomes reveal that the AMTPG-Jaya is able to reach an optimal solution with brilliant convergence speed. In addition, a robustness examination is implemented to evaluate the reliability of the AMTPG-Jaya solver. The simulation results disclose the dominance and potential of the AMTPG-Jaya over many solvers recently stated in the previous publications with regard to solution quality and validity. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
AMTPG-Jaya
Optimum power flow
Meta-heuristic algorithms
Optimization
Artificial intelligence
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6.6
论文数:
1.4W
被引数:
4.8W
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