风电机组齿轮箱的多变量时间序列故障预警

doi:10.11772/j.issn.1001-9081.2018102087

计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 1229-1233.DOI: 10.11772/j.issn.1001-9081.2018102087

• 应用前沿、交叉与综合 • 上一篇下一篇

风电机组齿轮箱的多变量时间序列故障预警

刘帅¹, 刘长良^1,2, 甄成刚¹

1. 华北电力大学控制与计算机工程学院, 北京 102206;
2. 新能源电力系统国家重点实验室(华北电力大学), 北京 102206

收稿日期:2018-10-15 修回日期:2018-12-09 发布日期:2019-04-10 出版日期:2019-04-10
通讯作者: 刘长良
作者简介:刘帅(1990-),男,河北安国人,博士研究生,主要研究方向:风电机组故障预警;刘长良(1965-),男,河北沧州人,教授,博士,主要研究方向:系统建模与仿真、故障诊断;甄成刚(1964-),男,河北藁城人,教授,博士,主要研究方向:故障预警、故障诊断。
基金资助:
北京市自然科学基金资助项目（4182061）；中央高校基本科研业务费专项资金资助项目（2018ZD05，9163116001）。

Multivariate time series fault warning for wind turbine gearbox

LIU Shuai¹, LIU Changliang^1,2, ZHEN Chenggang¹

1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University), Beijing 102206, China

Received:2018-10-15 Revised:2018-12-09 Online:2019-04-10 Published:2019-04-10
Supported by:
This work is partially supported by the Natural Science Foundation of Beijing (4182061), the Fundamental Research Funds for the Central Universities (2018ZD05, 9163116001).

摘要/Abstract

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。

关键词: 风电机组, 故障预警, 犹豫模糊集, 帝国竞争算法, 动态时间规整

Abstract: 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.

Key words: wind turbine, fault warning, Hesitant Fuzzy Set (HFS), imperialist competitive algorithm, Dynamic Time Warping (DTW)

中图分类号:

TP206.3

刘帅, 刘长良, 甄成刚. 风电机组齿轮箱的多变量时间序列故障预警[J]. 计算机应用, 2019, 39(4): 1229-1233.

LIU Shuai, LIU Changliang, ZHEN Chenggang. Multivariate time series fault warning for wind turbine gearbox[J]. Journal of Computer Applications, 2019, 39(4): 1229-1233.

参考文献

[1] 戴巨川, 袁贤松, 刘德顺, 等. 基于SCADA系统的大型直驱式风电机组机舱振动分析[J]. 太阳能学报, 2015, 36(12):2895-2905. (DAI J C, YUAN X S, LIU D S, et al. Vibration analysis of large direct drive wind turbine nacelle based on SCADA system[J]. Acta Energiae Solaris Sinica, 2015, 36(12):2895-2905.)
[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, 201434(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. (LIU S, 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]. IEEE Transactions 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 job-shop 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.

风电机组齿轮箱的多变量时间序列故障预警

Multivariate time series fault warning for wind turbine gearbox

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	张国辉, 陆熙熙, 胡一凡, 孙靖贺. 基于改进帝国竞争算法的柔性作业车间机器故障重调度[J]. 计算机应用, 2021, 41(8): 2242-2248.
[2]	沈忱, 邰凌翔, 彭煜玮. 面向自动参数调优的动态负载匹配方法[J]. 计算机应用, 2021, 41(3): 657-661.
[3]	王贵林, 李斌. 受春秋战国史实启发的帝国竞争改进算法[J]. 计算机应用, 2021, 41(2): 470-478.
[4]	夏伦腾, 张莉. 基于K近邻和动态时间规整算法的盲人物联网手杖系统[J]. 计算机应用, 2020, 40(8): 2441-2448.
[5]	陈孟辉, 刘俊麟, 徐健锋, 李向军. 求解旅行商问题的多样化搜索帝国竞争算法[J]. 计算机应用, 2019, 39(10): 2992-2996.
[6]	李强, 于凤芹. 改进音高轮廓创建和选择的旋律提取算法[J]. 计算机应用, 2018, 38(8): 2411-2415.
[7]	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.
[8]	谢小雨, 刘喆颉. 基于肌电信号和加速度信号的动态手势识别方法[J]. 计算机应用, 2017, 37(9): 2700-2704.
[9]	张明洋, 陈剑, 闻英友, 赵宏, 王玉刚. 基于动态时间规整距离指纹匹配的Wi-Fi网络室内定位算法[J]. 计算机应用, 2017, 37(6): 1550-1554.
[10]	朱轮. 基于后悔理论和证据理论的犹豫模糊决策方法[J]. 计算机应用, 2017, 37(2): 540-545.
[11]	高志方, 赖雨晴, 彭定洪. 云计算安全评估的区间犹豫模糊灰色妥协关联分析方法[J]. 计算机应用, 2017, 37(10): 2847-2853.
[12]	林杨, 黎元生, 王应明. 属性权重完全未知的犹豫模糊双边匹配决策[J]. 计算机应用, 2016, 36(8): 2268-2273.
[13]	林炀, 江育娥, 林劼. 基于分布式架构的时间序列局部相似检测算法[J]. 计算机应用, 2016, 36(12): 3285-3291.
[14]	庞永春, 孙子文, 王尧. 基于手机触摸屏传感器多点触摸身份认证算法[J]. 计算机应用, 2015, 35(6): 1780-1784.
[15]	周治平, 苗敏敏. 改进的马氏距离动态时间规整手势认证方法[J]. 计算机应用, 2015, 35(5): 1467-1470.