准确的预测风速对于风电场的安全运行和高效发电具有重要意义。 针对已有文献在风速预测问题中采用的单一分解 策略存在固有缺陷、优化预测模型效果不稳定等问题,提出了一种融合两阶段分解与 iJaya-ELM 的混合预测模型。 首先,对原 始风速序列进行 ICEEMDAN 分解,得到 12 个分量后基于排列熵熵值重构为高频项、中频项与低频项;随后对高频项进行奇异 谱分解滤去序列噪声;提出一种改进的 Jaya 算法 iJaya,利用 iJaya 算法获取极限学习机 ELM 的最优连接权值与阈值,最后将各 个分量的预测结果线性集成得到最终结果。 以我国甘肃地区风电场风速数据进行模型验证,并利用新疆地区数据集测试其鲁 棒性与通用性。 实验结果表明,iJaya 算法具有较强的寻优精度与稳定性,两阶段分解能够深度挖掘风速序列的特征;该混合模 型能够有效提升风速预测精度,平均绝对误差与均方误差分别为 0. 067 9 和 0. 134 5。
Accurate prediction of wind speed is of great significance for safe operation and efficient power generation of wind farms. Aiming at the inherent defects of the single decomposition strategy used in existing literatures in wind speed prediction and the unstable effect of the optimized prediction model, a hybrid prediction model combining two-stage decomposition and iJaya-ELM is proposed. First, ICEEMDAN decomposition is performed on the original wind speed sequence, and 12 components are obtained, and reconstructed into high frequency terms, middle frequency terms and low frequency terms based on the permutation entropy. Then, the high frequency term is filtered by singular spectrum decomposition to remove the sequence noise. An improved Jaya algorithm, iJaya, is proposed to obtain the optimal connection weights and thresholds of ELM. Finally, the predictive results of each component are linearly integrated to obtain the final results. The model is validated by wind speed data of wind farm in Gansu province of China, and its robustness and universality are tested by wind speed data of Xinjiang region. The experimental results show that the iJaya algorithm is of strong optimization accuracy and stability, and the two-stage decomposition can deeply excavate the characteristics of wind speed series. The hybrid model can effectively improve the wind speed prediction accuracy, and the average absolute error and mean square error are 0. 067 9 and 0. 134 5, respectively.
王逸文,王维莉,刘贤超,胡炜琴.融合两阶段分解与 iJaya-ELM 的短期风速预测模型[J].电子测量与仪器学报,2023,37(7):186-195复制