风电机组齿轮箱的多变量时间序列故障预警
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摘要
针对风电机组故障预警中,原始动态时间规整(DTW)算法无法有效度量风电机组多变量时间序列数据之间距离的问题,提出一种基于犹豫模糊集的动态时间规整(HFS-DTW)算法。该算法是原始DTW算法的一种扩展算法,可对单变量和多变量时间序列数据进行距离度量,且精度与速度较原始DTW算法更优。以子时间序列相似度距离为目标函数,使用帝国竞争算法(ICA)优化了HFS-DTW算法中的子序列长度和步距参数。算例研究表明与仅DTW算法和非参数最优的HFS-DTW算法相对比,参数最优的HFS-DTW可挖掘更多的多维特征点信息,输出的多维特征点相似序列具有更丰富细节;且基于所提算法可提前10天预警风电机组齿轮箱故障。
For wind turbine fault warning, original Dynamic Time Warping(DTW) algorithm cannot measure the distance effectively between two multivariate time series data of wind turbines. Aiming at this problem, a DTW algorithm based on Hesitation Fuzzy Set(HFS-DTW) was proposed. The algorithm is an extended algorithm of the original DTW algorithm, which can measure the distance of both univariate and multivariate time series data, and has higher accuracy and speed compared to the original DTW algorithm. With the sub-sequence similarity distance applied as cost function, the length of sub-sequence and step parameters in HFS-DTW algorithm were optimized by using Imperialist Competitive Algorithm(ICA). The study shows that compared to the only DTW algorithm and the HFS-DTW algorithm with non-optimal parameter, the HFS-DTW with optimal parameter can mine more information on multi-dimensional feature point, and the output multi-dimensional feature point similar sequence has more details. And based on the proposed algorithm, the wind turbine gearbox fault can be warned 10 days in advance.
For wind turbine fault warning, original Dynamic Time Warping(DTW) algorithm cannot measure the distance effectively between two multivariate time series data of wind turbines. Aiming at this problem, a DTW algorithm based on Hesitation Fuzzy Set(HFS-DTW) was proposed. The algorithm is an extended algorithm of the original DTW algorithm, which can measure the distance of both univariate and multivariate time series data, and has higher accuracy and speed compared to the original DTW algorithm. With the sub-sequence similarity distance applied as cost function, the length of sub-sequence and step parameters in HFS-DTW algorithm were optimized by using Imperialist Competitive Algorithm(ICA). The study shows that compared to the only DTW algorithm and the HFS-DTW algorithm with non-optimal parameter, the HFS-DTW with optimal parameter can mine more information on multi-dimensional feature point, and the output multi-dimensional feature point similar sequence has more details. And based on the proposed algorithm, the wind turbine gearbox fault can be warned 10 days in advance.
引文
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[10]解本铭,韩明明,张攀,等.飞机牵引车语音识别的动态时间规整优化算法[J].计算机应用,2018,38(6):1771-1776,1789.(XIE B M,HAN M M,ZHANG P,et al.Optimization algorithm of dynamic time warping for speech recognition of aircraft towing vehicle[J].Journal of Computer Applications,2018,38(6):1771-1776,1789.)
[11]TORRA V,NARUKAWA Y.On hesitant fuzzy sets and decision[C]//Proceedings of the 2009 IEEE International Conference on Fuzzy Systems.Piscataway,NJ:IEEE,2009:1378-1382.
[12]XU Z,XIA M.Distance and similarity measures for hesitant fuzzy sets[J].Information Sciences,2011,181(11):2128-2138.
[13]GOU X,XU Z,LIAO H.Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making[J].Information Sciences,2017,388/389:225-246.
[14]ATASHPAZ-GARGARI E,LUCAS C.Imperialist competitive algorithm:an algorithm for optimization inspired by imperialistic competition[C]//CEC 2007:Proceedings of the 2007 IEEE Congress on Evolutionary computation.Piscataway,NJ:IEEE,2007:4661-4667.
[15]吕聪,魏康林.柔性车间调度问题的协作混合帝国算法[J].计算机应用,2018,38(7):1882-1887.(LYU C,WEI K L.Cooperative hybrid imperialist competitive algorithm for flexible jobshop scheduling problem[J].Journal of Computer Applications,2018,38(7):1882-1887.)
[16]杨小东,康雁,柳青,等.求解作业车间调度问题的混合帝国主义竞争算法[J].计算机应用,2017,37(2):517-522,552.(YANG X D,KANG Y,LIU Q,et al.Hybrid imperialist competitive algorithm for solving job-shop scheduling problem[J].Journal of Computer Applications,2017,37(2):517-522,552.)
[17]刘帅,刘长良.基于帝国竞争算法的主汽温控制系统参数优化研究[J].系统仿真学报,2017,29(2):368-373.(LIU S,LIU C L.Cascade PID parameter optimization of main steam temperature control system based on imperialist competitive algorithm[J].Journal of System Simulation,2017,29(2):368-373.)
[18]LIU S,LIU C.Scale-varying dynamic time warping based on hesitant fuzzy sets for multivariate time series classification[J].Measurement,2018,130:290-297.
[2]孙鹏,李剑,寇晓适,等.采用预测模型与模糊理论的风电机组状态参数异常辨识方法[J].电力自动化设备,2017,37(8):90-98.(SUN P,LI J,KOU X S,et al.Wind turbine status parameter anomaly detection based on prediction models and fuzzy theory[J].Electric Power Automation Equipment,2017,37(8):90-98.)
[3]尹诗,余忠源,孟凯峰,等.基于非线性状态估计的风电机组变桨控制系统故障识别[J].中国电机工程学报,2014 34(增刊1):160-165.(YIN S,YU Z Y,MENG K F,et al.Fault identification of pitch control system of wind turbine based on nonlinear state estimation[J].Proceedings of the CSEE,2014,34(S1):160-165.)
[4]BANGALORE P,PATRIKSSON M.Analysis of SCADA data for early fault detection,with application to the maintenance management of wind turbines[J].Renewable Energy,2018,115:521-532.
[5]de la HERMOSA G-C R R.Wind farm monitoring using Mahalanobis distance and fuzzy clustering[J].Renewable Energy,2018,123:526-540.
[6]刘帅,刘长良,甄成刚,等.基于群体多维相似性的风机齿轮箱预警策略[J].仪器仪表学报,2018,39(1):180-189.(LIUS,LIU C L,ZHEN C G,et al.Fault warning strategy of wind turbines gearbox based on group multi-dimensional similarity[J].Chinese Journal of Scientific Instrument,2018,39(1):180-189.)
[7]HERP J,RAMEZANI M H,BACH-ANDERSEN M,et al.Bayesian state prediction of wind turbine bearing failure[J].Renewable Energy,2018,116:164-172.
[8]WANG Y,MA X,QIAN P.Wind turbine fault detection and identification through PCA-based optimal variable selection[J].IEEETransactions on Sustainable Energy,2018,9(4):1627-1635.
[9]ITAKURA F.Minimum prediction residual principle applied to speech recognition[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1975,23(1):67-72.
[10]解本铭,韩明明,张攀,等.飞机牵引车语音识别的动态时间规整优化算法[J].计算机应用,2018,38(6):1771-1776,1789.(XIE B M,HAN M M,ZHANG P,et al.Optimization algorithm of dynamic time warping for speech recognition of aircraft towing vehicle[J].Journal of Computer Applications,2018,38(6):1771-1776,1789.)
[11]TORRA V,NARUKAWA Y.On hesitant fuzzy sets and decision[C]//Proceedings of the 2009 IEEE International Conference on Fuzzy Systems.Piscataway,NJ:IEEE,2009:1378-1382.
[12]XU Z,XIA M.Distance and similarity measures for hesitant fuzzy sets[J].Information Sciences,2011,181(11):2128-2138.
[13]GOU X,XU Z,LIAO H.Hesitant fuzzy linguistic entropy and cross-entropy measures and alternative queuing method for multiple criteria decision making[J].Information Sciences,2017,388/389:225-246.
[14]ATASHPAZ-GARGARI E,LUCAS C.Imperialist competitive algorithm:an algorithm for optimization inspired by imperialistic competition[C]//CEC 2007:Proceedings of the 2007 IEEE Congress on Evolutionary computation.Piscataway,NJ:IEEE,2007:4661-4667.
[15]吕聪,魏康林.柔性车间调度问题的协作混合帝国算法[J].计算机应用,2018,38(7):1882-1887.(LYU C,WEI K L.Cooperative hybrid imperialist competitive algorithm for flexible jobshop scheduling problem[J].Journal of Computer Applications,2018,38(7):1882-1887.)
[16]杨小东,康雁,柳青,等.求解作业车间调度问题的混合帝国主义竞争算法[J].计算机应用,2017,37(2):517-522,552.(YANG X D,KANG Y,LIU Q,et al.Hybrid imperialist competitive algorithm for solving job-shop scheduling problem[J].Journal of Computer Applications,2017,37(2):517-522,552.)
[17]刘帅,刘长良.基于帝国竞争算法的主汽温控制系统参数优化研究[J].系统仿真学报,2017,29(2):368-373.(LIU S,LIU C L.Cascade PID parameter optimization of main steam temperature control system based on imperialist competitive algorithm[J].Journal of System Simulation,2017,29(2):368-373.)
[18]LIU S,LIU C.Scale-varying dynamic time warping based on hesitant fuzzy sets for multivariate time series classification[J].Measurement,2018,130:290-297.
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