STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Thermal Engineering and Applications

Optimizing Sizes And Locations Of Wind Turbines In Ieee 33-Node Distribution System For Loss Reduction By Using Moutaint Gazelle Algorithm

  1. Nguyen Van Thanh

Abstract

This study applies a novel meta-heuristic, called Moutaint gazelle algorithm (MGO) to determine the optimal sizes and locations of wind turbines to place in distribution networks for loss reduction. The IEEE 33-bus distribution system is a test system for conducting the whole study. The results obtained by MGO are compared to those obtained by another meta-heuristic algorithm, called Artificial Hummingbird Algorithm (AHA). The comparison between the two methods indicated that, MGO is more effective than AHA in terms of minimum, average, and maximum power loss values. Furthermore, in the best runs, MGO not only outperforms AHA in terms of convergence speed but also determines a lower power loss value. The evidence shows that MGO is a highly effective method, and it is strongly recommended to solve the problems of integrating renewable energy sources into the distribution grid.

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