基于改进鸡群算法的风能资源评估

Wind Energy Resource Assessment Based on Modified Chicken Swarm Optimization

研究了一种改进的鸡群(CSO)算法,引入了自我学习系数和学习因子提高小鸡和母鸡的学习能力,用于寻找形状参数k和尺度参数c的最优值。利用标准测试函数验证改进后的CSO算法的有效性。将改进后CSO算法计算得到k和c的最优值用于风能评估,并与等能量密度法的评估结果进行比较。结果表明,改进后的CSO算法的评估结果与实测数据统计更为接近。

A modified chicken swarm optimization （CSO） is studied, which introduces self-learning coefficient and learning factor to improve chicken and hen learning ability. It is used to find the optimal values of shape parameter k and scale parameter c. The standard test functions are used to verify the effectiveness of the modified CSO. The optimal values of k and c calculated by the modified CSO are applied to wind energy assessment. The results are compared with the results of the equal energy density method, which shows that modified CSO achieves the assessment values with less error to the statistic results.