Optimal power flow improvement using a hybrid teaching-learning-based optimization and pattern search
Автор: Belkacem Mahdad, Kamel Srairi
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 3 vol.10, 2018 года.
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In this paper a novel flexible planning strategy based on the teaching-learning-based optimization (TLBO) algorithm and pattern search algorithm (PS) is proposed to improve the security optimal power flow (SOPF) by minimizing the total fuel cost, total power loss and total voltage deviation considering critical load growth. The main particularity of the proposed hybrid method is that TLBO algorithm is adapted and coordinated dynamically with a local search algorithm (PS). In order validate the efficiency of the proposed strategy, it has been demonstrated on the Algerian 59-bus power system and the IEEE 118-bus for different objectives considering the integration of multi SVC devices. Considering the interactivity of the proposed combined method and the quality of the obtained results compared to the standard TLBO and to recent methods reported in the literature, the proposed method proves its ability for solving practical planning problems related to large power systems.
Power system planning, Optimal power flow, Teaching-Learning (TLBO), Pattern search, Hybrid method, Power loss, Voltage deviation, SVC
Короткий адрес: https://sciup.org/15016747
IDR: 15016747 | DOI: 10.5815/ijmecs.2018.03.07
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