ULTRA SHORT TERM WIND SPEED PREDICTION BASED ON WIND SPEED FLUCTUATION FEATURE EXTRACTION

Zhang Jiaan; Liu Dong; Liu Hui; Song Peng; Liu Jingbo; Wu Yuhui

doi:10.19912/j.0254-0096.tynxb.2020-1371

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (9) : 308-313. DOI: 10.19912/j.0254-0096.tynxb.2020-1371

ULTRA SHORT TERM WIND SPEED PREDICTION BASED ON WIND SPEED FLUCTUATION FEATURE EXTRACTION

Zhang Jiaan¹, Liu Dong¹, Liu Hui², Song Peng², Liu Jingbo², Wu Yuhui²

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Abstract

Aiming at the problem of low accuracy of wind speed prediction in wind farms, an ultra-short term wind speed prediction method based on wind speed fluctuation feature extraction is proposed. Firstly, the joint probability density model of wind speed and wind speed variation is established to analyze the uncertainty characteristics of wind speed. According to the wind speed fluctuation characteristics, the wind speed decomposition is recombined into fluctuation and trend quantity by using the values of Ensemble Empirical Mode Decomposition （EEMD） and Sample Entropy （SampEn） of wind speed components. The Artificial Fish Swarm Algorithm （AFSA） is used to optimize the Wavelet?Neural?Network （WNN） for trend prediction. The Improved Non-linear Autoregression （INARX） with external input neural network is used to predict the wind speed fluctuation, and then the predicted wind speed is obtained. Through the actual wind farm wind speed simulation prediction and comparison with many forecasting methods, the results show that the prediction error of this method is low, and can accurately predict the ultra-short term wind speed.

Key words

wind power / probability density function / neural networks / wind speed forecasting

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Zhang Jiaan, Liu Dong, Liu Hui, Song Peng, Liu Jingbo, Wu Yuhui. ULTRA SHORT TERM WIND SPEED PREDICTION BASED ON WIND SPEED FLUCTUATION FEATURE EXTRACTION[J]. Acta Energiae Solaris Sinica. 2022, 43(9): 308-313 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1371

References

[1] JIN Y Q, WU D M, JU P, et al.Modeling of wind speeds inside a wind farm with application to wind farm aggregate modeling considering LVRT characteristic[J]. IEEE transactions on energy conversion, 2020, 35(1): 508-519.
[2] 夏鹏, 刘文颖, 张尧翔, 等. 考虑风电高阶不确定性的分布式鲁棒优化调度模型[J]. 电工技术学报, 2020, 35(1): 189-200.
XIA P, LIU W Y, ZHANG Y X, et al.A distributionally robust optimization scheduling model considering higher-order uncertainty of wind power[J]. Transactions of China Electrotechnical Society, 2020, 35(1): 189-200.
[3] 姚万业, 黄璞, 姚吉行, 等. 一种基于深度学习的FRS-CLSTM风速预测模型[J]. 太阳能学报, 2020, 41(9): 324-330.
YAO W Y, HUANG P, YAO J X, et al.A frs-clstm wind speed prediction model based on deep learning[J]. Acta energiae solaris sinica, 2020, 41(9): 324-330.
[4] ZHU Q.Learning temporal and spatial correlations jointly: a unified framework for wind speed prediction[J]. IEEE transactions on sustainable energy, 2020, 11(1): 509-523.
[5] 潘超, 王典, 蔡国伟, 等. 考虑风速时空相关特性的元启发式支配预测模型[J]. 电网技术, 2020, 44(11): 4105-4114.
PAN C, WANG D, CAI G W, et al.Meta-heuristic dominance prediction model considering wind speed spatio-temporal correlation characteristics[J]. Power system technology, 2020, 44(11): 4105-4114.
[6] SUN Y, LI Z Y, YU X N, et al.Research on ultra-short-term wind power prediction considering source relevance[J]. IEEE access, 2020, 8: 147703-147710
[7] 潘超, 秦本双, 蔡国伟, 等. 一种新型模块化风速预测方法[J]. 太阳能学报, 2019, 40(8): 2196-2204.
PAN C, QIN B S, CAI G W, et al.A new modular forecasting method of wind speed[J]. Acta energiae solaris sinica, 2019, 40(8): 2196-2204.
[8] 向玲, 邓泽奇, 赵玥. 基于LPF-VMD和KELM的风速多步预测模型[J]. 电网技术, 2019, 43(12): 4461-4467.
XIANG L, DENG Z Q, ZHAO Y.Multi-step wind speed prediction model based on LPF-VMD and KELM[J]. Power system technology, 2019, 43(12): 4461-4467.
[9] ZHANG X L, LI J X, LIU X B, et al.Improved EEMD-based standardization method for developing long tree-ring chronologies[J]. Journal of forestry research, 2020, 31(6): 2217-2224.
[10] 张妍, 韩璞, 王东风, 等. 基于变分模态分解和LSSVM的风电场短期风速预测[J]. 太阳能学报, 2018, 39(1): 194-202.
ZHANG Y, HAN P, WANG D F, et al.Short-term prediction of wind speed for wind farm based on variational mode decomposition and LSSVM model[J]. Acta energiae solaris sinica, 2018, 39(1): 194-202.
[11] 潘超, 谭启德, 蔡国伟, 等. 基于递归量化分析的COA-SVR短期风速混合预测模型[J]. 电网技术, 2018, 42(8): 2373-2381.
PAN C, TAN Q D, CAI G W, et al.Hybrid short-term wind speed prediction model by COA-SVR based on recursive quantitative analysis[J]. Power system technology, 2018, 42(8): 2373-2381.
[12] 杨正瓴, 赵强, 吴炳卫, 等. 采用k近邻进行空间相关性超短期风速预测[J]. 电力自动化设备, 2019, 39(3): 175-181.
YANG Z L, ZHAO Q, WU B W, et al.Ultra-short term wind speed prediction based on spatial correlation by k-nearest neighbor[J]. Electric power automation equipment, 2019, 39(3): 175-181.
[13] 梁智, 孙国强, 俞娜燕, 等. 基于高斯过程回归和粒子滤波的短期风速预测[J]. 太阳能学报, 2020, 41(3): 45-51.
LIANG Z, SUN G Q, YU N Y, et al.Short-term wind speed forecasting based on gaussian process regression and particle filter[J]. Acta energiae solaris sinica, 2020, 41(3): 45-51.
[14] 王杰, 何山, 王维庆, 等. 基于混合灰色理论的风速预测研究[J]. 太阳能学报, 2018, 39(12): 3544-3549.
WANG J, HE S, WANG W Q, et al.Study on wind speed prediction based on mixed grey theory[J]. Acta energiae solaris sinica, 2018, 39(12): 3544-3549.
[15] KHORRAMDEL B, CHUNG C Y, SAFARI N, et al.A fuzzy adaptive probabilistic wind power prediction framework using diffusion kernel density estimators[J]. IEEE transactions on power systems, 2018, 33(6): 7109-7121.
[16] 王晨, 寇鹏. 基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测[J]. 电工技术学报, 2020, 35(13): 2723-2735.
WANG C, KOU P.Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2723-2735.
[17] ZHANG C.An improved elm model based on CEEMD-LZC and manifold learning for short-term wind power prediction[J]. IEEE access, 2019, 7: 121472-121481.
[18] 程启明, 陈路, 程尹曼, 等. 基于EEMD和LS-SVM模型的风电功率短期预测方法[J]. 电力自动化设备, 2018, 38(5): 27-35.
CHENG Q M, CHEN L, CHENG Y M, et al.Short-term wind power forecasting method based on EEMD and LS-SVM model[J]. Electric power automation equipment, 2018, 38(5): 27-35.
[19] ZHANG Z D, QIN H, YAO L Q, et al.Interval prediction method based on long-short term memory networks for system integrated of hydro, wind and solar power[J]. Energy procedia, 2019, 158: 6176-6182.