A data-driven evolutionary algorithm for wind farm layout optimization. (1st October 2020)

• Record Type: Journal Article
• Authors: Long, Huan; Li, Peikun; Gu, Wei
• Is Part Of: Energy. Volume 208(2020)
• Publication Date: 2020-10-01
• Subjects: Wind farm layout -- Wake effect -- Adaptive differential evolution -- Data-driven model -- Function approximation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118310
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The wind farm layout model is to optimize the location of wind turbines to maximize the power output of the wind farm. Due to the complexity of the wind farm layout problem, the computation of objective function costs lots of time. To reduce the high computational cost while maintaining the solution performance, a data-driven evolutionary algorithm is proposed. An adaptive differential evolution algorithm (ADE) is proposed as the solver of the wind farm layout model. The adaption mechanism of ADE benefits the automatic adjustment of parameters in the mutation and crossover operators to achieve the optimal solution. The general regression neural network (GRNN) algorithm builds the data-driven surrogate model. The data-driven surrogate model is trained and updated using the data generated by the evolutionary algorithm throughout the evolution process. Through the data-driven surrogate model, the objective function is fast approximated and the bad candidate solutions are identified. The algorithm efficiency is greatly improved by fast filtering the bad candidate solutions. The ADE-GRNN is compared to other three conventional optimization methods based on two different wind scenarios. The results show the super-performance of ADE-GRNN in complex situations in terms of power output and execution time. Highlights: The structure of the data-driven evolutionary algorithm, ADE-GRNN, is proposed. The objective function is approximated by the data-driven surrogate model. The GRNN trains the surrogate model to cut down the computational cost. The ADE algorithm with adaption mechanism improves the evolutionary success rate.