基于变分模式分解和向量自回归模型的波浪发电系统输出功率预测

罗琦; 杨俊华; 黄逸; 梁昊晖; 王超凡

doi:10.19912/j.0254-0096.tynxb.2021-1210

PDF(2627 KB)

太阳能学报 ›› 2023, Vol. 44 ›› Issue (3) : 291-297. DOI: 10.19912/j.0254-0096.tynxb.2021-1210

基于变分模式分解和向量自回归模型的波浪发电系统输出功率预测

罗琦, 杨俊华, 黄逸, 梁昊晖, 王超凡

作者信息 +

OUTPUT POWER PREDICTION OF WAVE POWER SYSTEM BASED ON VARIATIONAL MODEL DECOMPOSITION AND VECTOR AUTOREGRESSIVE MODEL

Luo Qi, Yang Junhua, Huang Yi, Liang Haohui, Wang Chaofan

Author information +

文章历史 +

摘要

为准确预测直驱式波浪发电系统的输出功率,提出基于变分模式分解和向量自回归模型预测方案。通过分析原始时间序列的相关性选择预测特征时间,应用变分模式分解方法将所选特征时间序列分解为不同子序列,经过单位根检验及差分运算,建立每个子序列的向量自回归模型,求和重构子序列模型预测结果获得所选特征的预测初值。建立了直驱式波浪发电系统的波能转换模型,仿真结果表明：所提方案模型具备合理性与可行性,模型预测结果稳定,预测精度高,预测趋势准确。

Abstract

Aiming to accurately predict the output power of direct-drive wave power generation systems, a prediction scheme based on variational pattern decomposition and vector autoregressive model is proposed. The predicted feature time is selected by analysing the correlation of the original time series, and the selected feature time series is decomposed into different sub-series by applying the variational mode decomposition method, which is subjected to unit root test and difference operation to establish a vector autoregressive model for each sub-series. A wave energy conversion model for direct-drive wave power generation system is established. The simulation results show that the proposed scheme model is reasonable and feasible, with stable model prediction results, high prediction accuracy and accurate prediction trends.

导出引用

罗琦, 杨俊华, 黄逸, 梁昊晖, 王超凡. 基于变分模式分解和向量自回归模型的波浪发电系统输出功率预测[J]. 太阳能学报. 2023, 44(3): 291-297 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1210

Luo Qi, Yang Junhua, Huang Yi, Liang Haohui, Wang Chaofan. OUTPUT POWER PREDICTION OF WAVE POWER SYSTEM BASED ON VARIATIONAL MODEL DECOMPOSITION AND VECTOR AUTOREGRESSIVE MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(3): 291-297 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1210

中图分类号： TM619

参考文献

[1] 洪岳, 潘剑飞, 刘云, 等. 直驱波浪能发电系统综述[J]. 中国电机工程学报, 2019, 39(7): 1886-1900.
HONG Y, PAN J F, LIU Y, et al.A review on linear generator based wave energy conversion systems[J]. Proceedings of the CSEE, 2019, 39(7): 1886-1900.
[2] 邱孟, 杨俊华, 林汇金, 等. 先进控制技术在波浪发电系统中的应用[J]. 电机与控制应用, 2021, 48(2): 13-21.
QIU M, YANG J H, LIN H J, et al.Application of modern control technology in wave energy conversion system[J]. Electric machines & control application, 2021, 48(2): 13-21.
[3] ERDEM E, SHI J.ARMA based approaches for forecasting the tuple of wind speed and direction[J]. Applied energy, 2011, 88(4): 1405-1414.
[4] ALI M, PRASAD R, XIANG Y, et al.Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms[J]. Renewable and sustainable energy reviews, 2020, 132: 110003.
[5] JAMA M, WAHYUDIE A, MEKHILEF S.Wave excitation force estimation using an electrical-based extended Kalman filter for point absorber wave energy converters[J]. IEEE access, 2020, 8: 49823-49836.
[6] 孙春顺, 王耀南, 李欣然. 小时风速的向量自回归模型及应用[J]. 中国电机工程学报, 2008, 28(14): 112-117.
SUN C S, WANG Y N, LI X R.A Vector Autoregression model of hourly wind speed and its applications in hourly wind speed forecasting[J]. Proceedings of the CSEE, 2008, 28(14): 112-117.
[7] 丁藤, 冯冬涵, 林晓凡, 等. 基于修正后ARIMA-GARCH模型的超短期风速预测[J], 电网技术, 2017, 41(6): 1808-1814.
DING T, FENG D H, LIN X F, et al.Ultra-short-term wind speed forecasting based on improved ARIMA-GARCH model[J]. Power system technology, 2017, 41(6): 1808-1814.
[8] 赵滨滨, 王莹, 王彬, 等. 基于ARIMA时间序列的分布式光伏系统输出功率预测方法研究[J]. 可再生能源, 2019, 37(6): 820-823.
ZHAO B B, WANG Y, WANG B, et al.Photovoltaic power prediction in distribution network based on ARIMA model time series[J]. Renewable energy resources, 2019, 37(6): 820-823.
[9] 朱乔木, 李弘毅, 王子琪, 等. 基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术, 2017, 41(12): 3797-3802.
ZHU Q M, LI H Y, WANG Z Q, et al.Short-term wind power forecasting based on LSTM[J]. Power system technology, 2017, 41(12): 3797-3802.
[10] 黄宝洲, 杨俊华, 卢思灵, 等. 基于改进粒子群优化神经网络算法的波浪捕获功率预测[J], 太阳能学报, 2021, 42(2): 302-308.
HUANG B Z, YANG J H, LU S L, et al.Wave capture power forecasting based on improved particle swarm optimization neural network algorithm[J]. Acta energiae solaris sinica, 2021, 42(2): 302-308.
[11] 李乐, 刘天琪, 陈振寰, 等. 基于EEMD和ARCH的风电功率超短期预测[J]. 电测与仪表, 2015, 52(18): 16-21.
LI L, LIU T Q, CHEN Z H, et al.Ultra-short-term wind power forecasting based on EEMD and ARCH[J]. Electrical measurement & instrumentation, 2015, 52(18): 16-21.
[12] 吴峰, 王飞, 顾康慧, 等. 基于MEEMD-ARIMA模型的波浪能发电系统输出功率预测[J]. 电力系统自动化, 2021, 45(1): 65-70.
WU F, WANG F, GU K H, et al.Output power prediction of wave energy generation system based on modified ensemble empirical mode decomposition-autoregressive integrated moving average model[J]. Automation of electric power systems, 2021, 45(1): 65-70.
[13] 周红标, 王乐, 卜峰, 等. 基于PSO-WPESN的短期电力负荷预测方法[J]. 电测与仪表, 2017, 54(6): 113-119.
ZHOU H B, WANG L, BU F, et al.Short-term power load forecasting method based on PSO-WPESN[J]. Electrical measurement & instrumentation, 2017, 54(6): 113-119.
[14] 盛四清, 金航, 刘长荣. 基于VMD-WSGRU的风电场发电功率中短期及短期预测[J]. 电网技术, 2021, 6(3): 897-904.
SHENG S Q, JIN H, LIU C R.Short-term and mid-short-term wind power forecasting based on VMD-WSGRU[J]. Power system technology, 2021, 6(3): 897-904.
[15] 阳曾, 丁施尹, 叶萌, 等. 基于变分模态分解和深度学习的短期电力负荷预测模型[J]. 电测与仪表, 2023,60(2):126-131, 146.
YANG Z, DING S Y, YE M, et al.Short-term load forecasting model based on VMD and LSTM[J]. Electrical measurement & instrumentation, 2023,60(2):126-131, 146.
[16] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[17] 康庆, 肖曦, 聂赞相, 等. 直驱型海浪发电系统输出功率优化控制策略[J]. 电力系统自动化, 2013, 37(3): 24-29.
KANG Q, XIAO X, NIE Z X, et al.An optimal control strategy for output power of the directly driven wave power generation system[J]. Automation of electric power systems, 2013, 37(3): 24-29.
[18] WU F, ZHANG X P, JU P, et al.Optimal control for AWS-based wave energy conversion system[J]. IEEE transactions on power systems, 2009, 24(4): 1747-1755.
[19] BERBIĆ J, OCVIRK E, CAREVIĆ D, et al.Application of neural networks and support vector machine for significant wave height prediction[J]. Oceanologia, 2017, 59(3): 331-349.
[20] UIHLEIN A, MAGAGNA D.Wave and tidal current energy-a review of the current state of research beyond technology[J]. Renewable and sustainable energy reviews, 2016, 58: 1070-1081.
[21] REIKARD G, ROBERTSON B, BIDLOT J.Wave energy worldwide: simulating wave farms, forecasting, and calculating reserves[J]. International journal of marine energy, 2017, 17: 156-185.