COMBINED ULTRA-SHORT-TERM POWER PREDICTION FOR WIND POWER HYDROGEN PRODUCTION TECHNOLOGY

Zhao Yuyang; Zhao Haoran; Tan Jianxin; Zhang Rengkai; Jing Yanwei; Sun Hexu

doi:10.19912/j.0254-0096.tynxb.2021-1266

PDF(2286 KB)

Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (3) : 162-168. DOI: 10.19912/j.0254-0096.tynxb.2021-1266

COMBINED ULTRA-SHORT-TERM POWER PREDICTION FOR WIND POWER HYDROGEN PRODUCTION TECHNOLOGY

Zhao Yuyang^1,2, Zhao Haoran¹, Tan Jianxin³, Zhang Rengkai¹, Jing Yanwei³, Sun Hexu^1,2

Author information +

History +

Abstract

A combined ultra-short-term wind power prediction strategy with high robustness based on least squares support vector machine (LSSVM) has been proposed, in order to solve the wind abandonment caused by wind power randomness and realize efficient hydrogen production under wide power fluctuation. Firstly, the original wind power data is decomposed into sub-modes with different bandwidth by variational modal decomposition (VMD), which reduces the influence of random noise and mode mixing significianly. Then dragonfly algorithm (DA) is introduced to optimize LSSVM kernel function and the combined ultra-short-term wind power prediction strategy which meets the time resolution and accuracy requirements of electrolytic cell control has been established finally. This model is validated by a wind power hydrogen production demonstration project output in Hebei Province. The superior prediction accuracy for high volatility wind power data is verified and the algorithm provides theoretical basis to improve the control of wind power hydrogen production system.

Key words

wind power prediction / wind power / hydrogen production / least square support vector machine / variational modal decomposition / dragonfly algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhao Yuyang, Zhao Haoran, Tan Jianxin, Zhang Rengkai, Jing Yanwei, Sun Hexu. COMBINED ULTRA-SHORT-TERM POWER PREDICTION FOR WIND POWER HYDROGEN PRODUCTION TECHNOLOGY[J]. Acta Energiae Solaris Sinica. 2023, 44(3): 162-168 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1266

References

[1] 余向阳, 赵怡茗, 杨宁宁, 等. 基于VMD-SE-LSSVM和迭代误差修正的光伏发电功率预测[J]. 太阳能学报, 2020, 41(2): 310-318.
YU X Y, ZHAO Y M, YANG N N, et al.Photovoltaic power generation forecasting based on VMD-SE-LSSVM and iterative error correction[J]. Acta energiae solaris sinica, 2020, 41(2): 310-318.
[2] 孙鹤旭, 李争, 陈爱兵, 等. 风电制氢技术现状及发展趋势[J]. 电工技术学报, 2019, 34(19): 4071-4083.
SUN H Y, LI Z, CHEN A B, et al.Current status and development trend of hydrogen production technology by wind power[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4071-4083.
[3] SHI J, DING Z, LEE W, et al.Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features[J]. IEEE transactions on smart grid, 2014, 5(1): 521-526.
[4] 杨茂, 杨宇. 基于小波包与LSSVM的短期光伏输出功率预测研究[J]. 可再生能源, 2019, 37(11): 1595-1602.
YANG M, YANG Y.Short-term photovoltaic output power prediction based on wavelet packet and LSSVM[J]. Renewable energy resources, 2019, 37(11): 1595-1602.
[5] 孙辉, 冷建伟. 基于结合混沌纵横交叉的PSO-DBN的短期光伏功率预测[J]. 电测与仪表, 2020, 57(6): 67-72.
SUN H, LENG J W.Short-term PV power prediction based on particle swarm optimization combined with chaos crossover for deep belief networks[J]. Electrical measurement & instrumentation, 2020, 57(6): 67-72.
[6] 周鹏, 董朝轶, 陈晓艳, 等. 基于阶梯式Tent混沌和模拟退火的樽海鞘群算法[J]. 电子学报, 2021, 49(9): 1724-1735.
ZHOU P, DONG C Y, CHEN X Y, et al.A salp swarm algorithm based on stepped tent chaos and simulated annealing[J]. Acta electronica sinica, 2021, 49(9): 1724-1735.
[7] ZHANG L Y, WANG J Z, NIU X S.Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm[J]. Sustainable energy technologies and assessments, 2021, 47: 10346.
[8] 邵志芳, 吴继兰. 基于动态电价风光电制氢容量配置优化[J]. 太阳能学报, 2020, 41(8) :227-235.
SHAO Z F, WU J L.Optimization of wind photoelectric hydrogen production capacity configuration based on dynamic electricity price[J]. Acta energiae solaris sinica, 2020, 41(8): 227-235.
[9] 沈小军, 聂聪颖, 吕洪. 计及电热特性的离网型风电制氢碱性电解槽阵列优化控制策略[J]. 电工技术学报, 2021, 36(3): 463-472.
SHEN X J, NIE C Y, LYU H.Coordination control strategy of wind power-hydrogen alkaline electrolyzer bank considering electrothermal characteristics[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 463-472.
[10] 王粟, 江鑫, 曾亮, 等. 基于VMD-DESN-MSGP模型的超短期光伏功率预测[J]. 电网技术, 2020, 44(3): 917-926.
WANG S, JIANG X, ZENG L, et al.Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model[J]. Power system technology, 2020, 44(3): 917-926.
[11] 梁智, 孙国强, 李虎成, 等. 基于VMD与PSO优化深度信念网络的短期负荷预测[J]. 电网技术, 2018, 42(2): 598-606.
LIANG Z,SUN G Q, LI H C, et al.Short-term load forecasting based on VMD and PSO optimized deep belief network[J]. Power system technology, 2018, 42(2): 598-606.
[12] 傅军栋, 陈俐, 康水华, 等. 基于蜻蜓算法和支持向量机的变压器故障诊断[J]. 华东交通大学学报, 2016, 33(4): 103-112.
FU J D, CHEN L, KANG S H, et al.Transformer fault diagnosis based on dragonfly optimization algorithm and support vector machine[J]. Journal of East China Jiaotong University, 2016, 33(4): 103-112.
[13] 赵倩, 郑贵林. 基于WD-LSSVM-LSTM模型的短期电力负荷预测[J]. 电测与仪表, 2023, 60(1): 1-7.
ZHAO Q, ZHENG G L.Short-term load forecasting based on WD-LSSVM-LSTM model[J]. Electrical measurement & instrumentation, 2023, 60(1): 1-7.
[14] 宋玉琴, 邓思成, 路彦刚. K值优化的VMD在轴承故障诊断中的应用[J]. 测控技术, 2019, 38(4): 117-121.
SONG Y Q, DENG S C, LU Y G.Application of K value optimized VMD in bearing fault diagnosis[J]. Measurement & control technology, 2019, 38(4): 117-121.