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Kanagasabai L.
Real Power Loss Reduction by Extreme Learning Machine Based Drongos Search Algorithm / Kanagasabai L. - URL: https://vestniken.bmstu.ru/catalog/math/compmath/1129.html (дата обращения: 11.03.2026). - DOI 10.18698/1812-3368-2024-1-41-62 // Вестник МГТУ им. Н. Э. Баумана. Сер. Естественные науки. - 2024. - № 1. -
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In this paper the Extreme learning machine (ELM) based Drongos search (DS) algorithm — ELMDS algorithm — is applied to solve the power loss lessening problem. Extreme learning machine is applied and learning speed of feed-forward neural networks is composed of input, hidden and output layer. Drongos search algorithm is a modern algorithm which is inspired on the elegance performance of Drongos. In expedition to control obscured place a Drongos j pursuit Drongos i. Formerly Drongos i do not sentient of the existence of the added Drongos, as a consequence to the cause of Drongos j is to accomplish. And in Fiddling "Dron-gos" i differentiate about the presence of Drongos j and it protector its nourishment, Drongos i calculatingly take an impulsive way to sentinel its nourishment. This show is replicated by employing an unpredictable evolution. Then care possibility is replaced by a vibrant care possibility for enrichment, which is adapted by the aptness supremacy of every contender solution. Levy flights are employed as a substitute of unswerving illogical activities to duplicate the dodging performance. In ELMDS algorithm input weight rate and concealed layer inception in ELM are logically optimized by the DS algorithm. Legitimacy of ELMDS algorithm is corroborated in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained Please cite this article as: Kanagasabai L. Real power loss reduction by extreme learning machine based Dron-gos search algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2024, no. 1 (112), pp. 41–62. EDN: FIMVMC
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№ 1. - 2024.
