Optimization of Wind Farm Layout for Maximum Energy Production by Stochastic Fractal Search

Abstract

The wind power plant designs are different from the design of other conventional power plants such as hydropower plants, thermal power plants, and nuclear power plants because the input fuel of these types of power plants is controllable. Wind power plants depend on the speed of wind energy. Therefore, the problem of optimizing the location of turbines in a wind farm to achieve maximum annual energy output (AEP) is of great interest. In this paper, the Stochastic Fractal Search (SFS) algorithm is proposed to optimize the arrangement of turbines in the wind farm to minimize the wake effect so that the wind farm achieves the maximum generating capacity and the highest power factor (CF). SFS represents a significant advancement in optimization techniques, offering robust, adaptable, and efficient solutions to complex problems like wind farm layout optimization. Its innovative use of fractional dynamics and stochastic processes distinguishes it from traditional methods, providing superior performance in many scenarios. The proposed method was tested on a standard case with three types of turbines with different capacities of 850kW, 1000kW, and 1500kW to confirm the suitability of the algorithm and select the most appropriate turbine type. The results of AEP and wake loss calculated by the SFS algorithm were superior compared to those obtained by the PSO algorithm for these three turbine types. The turbine with the highest CF will be selected for application in the wind farm. Therefore, the proposed SFS algorithm can be a potential method to deal with the problem of optimization of wind farm layout.