基于改进鲸鱼算法考虑尾流效应的风电场优化控制

Optimal Control of Wind Farm Based on Improved Whale Algorithm Considering Wake Effect

对于考虑尾流效应的风电场控制模型往往存在高维数、非线性、多参数耦合,传统寻优算法难以满足控制需求问题,因此引入一种适用于解决大规模复杂问题的鲸鱼优化算法(WOA)。针对算法迭代后期收敛速度慢,最优个体易局部聚集问题,通过立体混沌映射提高种群多样性,并提出一种新的收敛因子更新公式来协调算法全局探索与局部开发能力,最后引入改进混合蛙跳算法中最差蛙位置改变策略增强算法跳出局部最优能力。仿真对比5种算法求解风电场最优输出功率,结果证明所提算法具有更好的收敛速度与精度,可为考虑尾流效应的风电场功率输出优化控制研究提供借鉴。

Because the wind farm control models considering wake effects are often high-dimensional,nonlinear and multiparameter couplings,the traditional optimization algorithms cannot meet the control requirement.To solve this problem,a whale optimization algorithm(WOA)which is suitable for solving large-scale and complex problems is introduced.For the problem of slow convergence speed in the later iteration of the algorithm and local aggregation of the optimal individuals,the three-dimensional chaotic mapping is firstly used to increase the diversity of the population,then a new convergence factor update formula is proposed to coordinate the algorithm’s global exploration and local development capabilities,and finally the position change strategy of the worst frog in the improved hybrid leapfrog algorithm is introduced to enhance the algorithm’s ability to jump out of the local optimum.The simulation compares five algorithms to solve the optimal output power of wind farms,and the results prove that the proposed algorithm has better convergence speed and solution accuracy,which can provide reference for the research of wind farm power output optimization control considering the wake effect.