Beşkirli, Mehmet.Koç, Ismail.Kodaz, Halife.2020-03-262020-03-262019Beşkirli, M., Koc, I., Kodaz, H. (2019). Optimal Placement of Wind Turbines Using Novel Binary Invasive Weed Optimization. Tehnički vjesnik, 26(1), 56-63.1330-36511848-6339https://dx.doi.org/10.17559/TV-20170725231351https://hdl.handle.net/20.500.12395/38063Wind turbines - which are significant in terms of clean energy production globally - are environmentally friendly, consistent and economical systems. Wind turbines, due to developing technology, have become one of the most widely used renewable energy resources, and every country has worked to satisfy its electricity demands with the help of wind energy. As the importance of wind energy increases all around the world, the importance of wind turbine placement also rises. In this study, the aim was to position wind turbines over a certain area of a wind farm to obtain maximum turbine power with minimum investment cost, thereby achieving the highest power efficiency. The experimental studies were conducted over a 2x2 km area; this area was divided into a 10x10 grid, and a 20x20 grid for more efficient placement. Because these operations occurred in a binary search space, Invasive Weed Optimization (IWO) - normally used to solve unceasing optimization problems - was used in this study by obtaining fourteen different binary Invasive Weed Optimization (BIWO1 to BIWO14) algorithms with the help of ten different transfer functions (four from the sshaped family, four from the v-shaped family, two based on modulo 2, ceil, ceil-round, ceil-floor and round-floor). The proposed method was compared with other studies carried out in the binary search space found in published literature. As a result, it was seen that the proposed algorithm was an efficient algorithm for solving the problem of wind turbine placement to achieve an optimal placement.en10.17559/TV-20170725231351info:eu-repo/semantics/openAccessbinary invasive weed optimizationoptimizationrenewable energywind turbine placement problemArticle2615663Q3WOS:000458827900009Q4