基于组合神经网络的风机轮毂处短期风速预测

doi:10.3969/j.issn.1004-132X.2021.17.009

中国机械工程 ›› 2021, Vol. 32 ›› Issue (17): 2082-2089.DOI: 10.3969/j.issn.1004-132X.2021.17.009

基于组合神经网络的风机轮毂处短期风速预测

马军岩;袁逸萍;柴同;赵琴

新疆大学机械工程学院，乌鲁木齐，830047

出版日期:2021-09-10 发布日期:2021-09-28
通讯作者: 袁逸萍（通信作者），女，1973年生，教授、博士研究生导师。研究方向为计算机集成制造系统、工业工程、质量与可靠性等。发表论文50余篇。E-mail：yipingyuan@163.com。
作者简介:马军岩，女，1990年生，博士研究生。研究方向为风速预测、风力发电机的故障预测与健康管理。E-mail：majunyanbyr@163.com。
基金资助:
国家自然科学基金（71961029）；
新疆维吾尔自治区研究生科研创新项目（XJ2020G015）

Short Term Wind Speed Prediction of Wind Turbine Hubs Based on Combined Neural Network

MA Junyan;YUAN Yiping;CHAI Tong;ZHAO Qin

School of Mechanical Engineering，Xinjiang University，Urumqi，830047

Online:2021-09-10 Published:2021-09-28

摘要/Abstract

摘要： 综合考虑风电机组轮毂处历史风速规律和近期数值气象预报风速变化，对时间尺度为6 h以内的风电机组轮毂处风速进行预测。为提高风速骤变情况下短期风速预测精度，提出一种新的组合神经网络。针对信号分解后的不同频率子序列特点，采用深度卷积神经网络和门控循环递归单元对趋势项子序列进行预测，用建立的Elman循环递归神经网络对细节项子序列进行预测。利用降尺度后的数值气象预报风速来判断风速骤变拐点，采用广义自回归条件异方差模型对非拐点风速预测值进行修正。最后，利用新疆某风电场实际风速数据进行实验，以平均绝对误差、均方误差及平均绝对百分比误差计算确定性预测精度，以覆盖率和区间宽度计算不确定性预测精度，结果验证了所提算法的有效性。

关键词: 短期风速预测, 组合模型, 风速拐点, 误差修正

Abstract: The wind speed less than 6 hours at the wind turbine hubs was predicted by comprehensively considering the historical wind speed rules and wind speed changes in recent numerical weather forecasts. In order to improve the accuracy of short-term wind speed prediction under the condition of sudden changes of wind speeds， a new combined neural network was proposed. According to the characteristics of different frequency subsequences obtained by signal decomposition， the deep convolution neural network and the gated cyclic recursive unit were used to predict the low-frequency subsequence， and Elman recurrent neural network was established to predict the high-frequency sequence. The downscaling numerical weather prediction wind speed was used to identify inflection points， and the generalized autoregressive conditional heteroscedasticity model was used to modify the non inflection point of wind speed. Finally， the actual wind speed data of a wind farm in Xinjiang were used for the experiments. The mean absolute errors， mean square errors and mean absolute percentage errors were used to calculate the deterministic prediction accuracy， and the coverage rate and interval width were used to calculate the uncertainty prediction accuracy， which verified the effectiveness of the proposed algorithm.

Key words: short term wind speed prediction, combination model, wind speed inflection point, error correction

中图分类号:

TM614

马军岩, 袁逸萍, 柴同, 赵琴. 基于组合神经网络的风机轮毂处短期风速预测[J]. 中国机械工程, 2021, 32(17): 2082-2089.

MA Junyan, YUAN Yiping, CHAI Tong, ZHAO Qin. Short Term Wind Speed Prediction of Wind Turbine Hubs Based on Combined Neural Network[J]. China Mechanical Engineering, 2021, 32(17): 2082-2089.

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链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2021.17.009

http://www.cmemo.org.cn/CN/Y2021/V32/I17/2082

参考文献

［1］TANER T， DEMIRCI O K. Energy and Economic Analysis of the Wind Turbine Plants Draft for the Aksaray City［J］. Biochimie， 2014， 2（3）：82-85.
［2］COSTA I C， VENTURINI L F， DA ROSA M A. Wind Speed Severity Scale Model Aapplied to Overhead Line Reliability Simulation［J］. Electric Power Systems Research， 2019， 171：240-250.
［3］WANG Hui， LIU Da， WANG Jilong. Ultra-short-term Wind Speed Prediction Based on Spectral Clustering and Optimized Extreme Learning Machine［J］. Power System Technology， 2015， 39（5）：1307-1314.
［4］LOUKA P， GALANIS G， SIEBERT N， et al. Improvements in Wind Speed Forecasts for Wind Power Prediction Purposes Using Kalman Filtering［J］. Journal of Wind Engineering & Industrial Aerodynamics， 2008， 96（12）：2348-2362.
［5］CASSOLA F， BURLANDO M. Wind Speed and Wind Energy Forecast through Kalman Filtering of Numerical Weather Prediction Model Output［J］. Applied Energy， 2012， 99：154-166.
［6］HUA Shenbing， WANG Shu， JIN Shuanglong， et al. Wind Speed Optimisation Method of Numerical Prediction for Wind Farm Based on Kalman Filter Method［J］. The Journal of Engineering， 2017， 13：1146-1149.
［7］ZHAO X， LIU J， YU D， et al. One-day-ahead Probabilistic Wind Speed Forecast Based on Optimized Numerical Weather Prediction Data［J］. Energy Conversion and Management， 2018， 164：560-569.
［8］DING M， ZHOU H， XIE H， et al. A Gated Recurrent Unit Neural Networks Based Wind Speed Error Correction Model for Short-term Wind Power Forecasting［J］. Neurocomputing， 2019， 365：54-61.
［9］WANG H， HAN S， LIU Y， et al. Sequence Transfer Correction Algorithm for Numerical Weather Prediction Wind Speed and Its Application in a Wind Power Forecasting System［J］. Applied Energy， 2019， 237：1-10.
［10］LIU H， TIAN H Q， LIY F. Comparison of Two New ARIMA-ANN and ARIMA-Kalman Hybrid Methods for Wind Speed Prediction［J］. Applied Energy， 2012， 98：415-424.
［11］SHUKUR O B， LEE M H. Daily Wind Speed Forecasting through Hybrid KF-ANN Model Based on ARIMA［J］. Renewable Energy， 2015， 76：637-647.
［12］JIA X， DI Y， FENG J， et al. Adaptive Virtual Metrology for Semiconductor Chemical Mechanical Planarization Process Using GMDH-type Polynomial Neural Networks［J］. Journal of Process Control， 2018， 62：44-54.
［13］ZHU S， YUAN X， XU Z， et al. Gaussian Mixture Model Coupled Recurrent Neural Networks for Wind Speed Interval Forecast［J］. Energy Conversion and Management， 2019， 198：111772.
［14］LIU H， MI X W， LI Y F. Wind Speed Forecasting Method Based on Deep Learning Strategy Using Empirical Wavelet Transform， Long Short Term Memory Neural Network and Elman Neural Network［J］. Energy Conversion and Management， 2018， 156：498-514.
［15］MONFARED M， RASTEGAR H， KOJABADI H M. A New Strategy for Wind Speed Forecasting Using Artificial Intelligent Methods［J］. Renewable Energy， 2009， 34：845-848.
［16］POURMOUSAVI KANI S A， ARDEHALI M M. Very Short-term Wind Speed Prediction：a New Artificial Neural Network-Markov Chain Model［J］. Energy Conversion and Management， 2011， 52（1）：738-745.
［17］CHEN K， YU J. Short-term Wind Speed Prediction Using an Unscented Kalman Filter Based State-space Support Vector Regression Approach［J］. Applied Energy， 2014， 113：690-705.
［18］ZHANG Y， CHEN B， PAN G， et al. A Novel Hybrid Model Based on VMD-WT and PCA-BP-RBF Neural Network for Short-term Wind Speed Forecasting［J］. Energy Conversion and Management， 2019， 195：180-197.
［19］MA Z， CHEN H， WANG J， et al. Application of Hybrid Model Based on Double Decomposition， Error Correction and Deep Learning in Short-term Wind Speed Prediction［J］. Energy Conversion and Management， 2020， 205：112345.
［20］WU Z， HUANG N E. Ensemble Empirical Mode Decomposition：a Noise Assisted Data Analysis Method［J］. Advances in Adaptive Data Analysis， 2009， 1（1）：1-41.
［21］ZHANG W， QUZ， ZHANG K， et al. A Combined Model Based on CEEMDAN and Modified Flower Pollination Algorithm for Wind Speed Forecasting［J］. Energy Conversion and Management， 2017， 136：439-451.
［22］HAO Y， TIAN C. The Study and Application of a Novel Hybrid System for Air Quality Early-warning［J］. Applied Soft Computing， 2019， 74：729-746.
［23］LIU H， CHEN C， TIAN H Q， et al. A Hybrid Model for Wind Speed Prediction Using Empirical Mode Decomposition and Artificial Neural Networks［J］. Renewable Energy， 2012， 48：545-556.
［24］LIU H， TIAN H Q， LIY F. Comparison of New Hybrid FEEMD-MLP， FEEMD-ANFIS， Wavelet Packet-MLP and Wavelet Packet-ANFIS for Wind Speed Predictions［J］. Energy Conversion and Management， 2015， 89：1-11.
［25］DRAGOMIRETSKIY K， ZOSSO D. Variational Mode Decomposition［J］. IEEE Transactions on Signal Processing， 2014， 62（3）：531-544.
［26］KINGMA D P， BA J. Adam：a Method for Stochastic Optimization［C］∥International Conference on Learning Representations. San Diego， 2015：1-15.
［27］WANG J， ZHANG W， LI Y， et al. Forecasting Wind Speed Using Empirical Mode Decomposition and Elman Neural Network［J］. Applied Soft Computing， 2014， 23：452-459.
［28］JIANG Y， HUANG G， PENG X， et al. A Novel Wind Speed Prediction Method：Hybrid of Correlation-aided DWT， LSSVM and GARCH［J］. Journal of Wind Engineering and Industrial Aerodynamics， 2018， 174：28-38.
［29］刘晓楠，周介圭，贾宏杰，等. 基于非参数核密度估计与数值天气预报的风速预测修正方法［J］. 电力自动化设备， 2017， 37（10）：15-20.
LIU Xiaonan， ZHOU Jiegui， JIA Hongjie， et al. Correction Method of Wind Speed Prediction Based on Non-parametric Kernel Density Estimation and Numerical Weather Prediction［J］. Electric Power Automation Equipment， 2017， 37（10）：15-20.

基于组合神经网络的风机轮毂处短期风速预测

Short Term Wind Speed Prediction of Wind Turbine Hubs Based on Combined Neural Network

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