摘要
为了提高风电功率预测的精度,提出了一种基于总体平均经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)、排列熵(permutation entropy,PE)、小波包分解(wavelet packet decomposition,WPD)和多目标优化的超短期风电功率预测方法。首先,利用CEEMDAN、PE和WPD构成的信号处理方法降低原始风电信号的随机性和波动性;然后,将分解后的子分量输入到长短期记忆(long short-term memory,LSTM)神经网络,并且利用精英T分布麻雀优化算法(elite t-distribution sparrow optimization algorithm,ETSSA)优化LSTM的隐藏层单元数,提升LSTM网络的预测性能;最后,建立多目标优化损失函数,将准确率、稳定度和合格率3个优化目标同时加入到损失函数中,综合提升模型的预测性能。对内蒙古某地区风力发电场的实测数据进行实验分析结果表明,与其他经典预测方法相比,所提方法提升风电功率预测性能有显著效果,并且在不同季节风况下预测效果良好。
In order to improve the accuracy of wind power prediction,an ultra-short-term wind power prediction based on the overall average empirical mode decomposition(CEEMDAN),the Permutation Entropy(PE),the Wavelet Packet Decomposition(WPD) and the multi-objective optimization is proposed.First,the signal processing method consisting of the CEEMDAN,the PE and the WPD is used to reduce the randomness and volatility of the original wind power signals;then,the decomposed subcomponents are fed into the Long/Short-Term Memory(LSTM) network,and an improved Elite T-distribution Sparrow Optimization Algorithm(ETSSA) is used to optimize the number of hidden layer units of the LSTM to improve the prediction performance of the LSTM network;finally,the loss function is optimized with the three optimization objectives of accuracy,stability and pass rate added into it at the same time to improve the prediction performance of the model comprehensively.The experimental analysis of the measured data from a wind farm in a region in Inner Mongolia shows that,compared with other classical prediction methods,the proposed method has a more significant effect on improving the wind power prediction performance and a better prediction effect under different seasonal wind conditions.
作者
常雨芳
杨子潇
潘风
唐杨
黄文聪
CHANG Yufang;YANG Zixiao;PAN Feng;TANG Yang;HUANG Wencong(Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System(Hubei University of Technology),Wuhan 430068,Hubei Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2023年第12期5015-5025,共11页
Power System Technology
基金
国家自然科学基金项目(61903129)
湖北工业大学博士启动基金研究项目(BSQD2020012)。
关键词
超短期风电功率预测
总体平均经验模态分解
排列熵
小波包分解
长短期记忆神经
精英T分布麻雀优化算法
多目标优化
ultra-short-term wind power prediction
complete ensemble empirical mode decomposition with adaptive noise
permutation entropy
wavelet packet decomposition
long/short-term memory
elite t-distribution sparrow optimization algorithm
multi-objective optimization
