针对HHO算法存在搜索过程调整不够灵活,不能针对性地进行阶段性搜索,有时会陷入局部最优使算法搜索精度相对较差等问题,提出了一种基于改进哈里斯鹰优化(IHHO)算法的参数辨识方法。对HHO算法进行了两项改进:引入柔性递减策略,在迭代初期扩大全局搜索范围,在迭代后期延长局部搜索时间,从而加强了初期的全局搜索能力和后期的局部搜索能力;引入黄金正弦法,不但增加了种群的多样性,减少算法陷入局部最优的可能性,并且缩小了搜索空间,提高了寻优效率。应用于光伏电池工程模型的参数辨识中,IHHO算法比其他算法得到的辨识结果更为精确,辨识结果与实测数据拟合度更高,IHHO算法能够在不同环境下对光伏电池的工程模型进行准确的参数辨识。
Abstract
Aiming at the problem that the adjustment of HHO algorithm is not flexible enough in the search process, periodic search cant be carried out pertinently, and sometimes the algorithm falls into the local optimal leading to relatively poor search accuracy, a parameter identification method based on improved harris hawks optimization (IHHO) algorithm is proposed. Two improvements are made to HHO algorithm. The flexible decline strategy is introduced to expand the global search scope at the beginning of the iteration and to extend the local search time at the end of the iteration, which strengthens the global search ability at the initial stage and the local search ability at the later stage. The introduction of golden sine method not only increases the diversity of the population, reduces the possibility of the algorithm falling into local optimization, but also reduces the search space and improves the efficiency of optimization. When applied to the parameter identification of photovoltaic cell engineering model, IHHO algorithm is more accurate than other algorithms, and the identification results are more consistent with the measured data, which shows that IHHO algorithm can accurately identify the parameters of photovoltaic cell engineering model in different environments.
关键词
光伏电池 /
工程模型 /
参数辨识 /
改进哈里斯鹰优化算法 /
柔性递减 /
局部最优
Key words
metrology /
photovoltaic cells /
engineering model /
parameter identification /
IHHO algorithm /
flexibility decline /
local optimization
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基金
河北省自然科学基金(F2020203014)
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