基于运行规律和TICC算法的风电SCADA高维时序数据聚类方法

doi:10.3901/JME.2022.23.196

机械工程学报 ›› 2022, Vol. 58 ›› Issue (23): 196-207.doi: 10.3901/JME.2022.23.196

扫码分享

基于运行规律和TICC算法的风电SCADA高维时序数据聚类方法

肖钊¹, 邓杰文¹, 刘晓明², 段书用², 许守亮³

1. 湖南科技大学机电工程学院湘潭 411201;
2. 省部共建电工装备可靠性与智能化国家重点实验室(河北工业大学) 天津 300401;
3. 华电郑州机械设计研究院有限公司郑州 450046

收稿日期:2022-03-07 修回日期:2022-10-10 出版日期:2022-12-05 发布日期:2023-02-08
通讯作者: 刘晓明(通信作者),女,1968年出生,博士,教授,博士研究生导师。主要研究方向为智能电器、现代电力设备可靠性与智能化。E-mail:liuxiaoming@hebut.edu.cn
作者简介:肖钊,男,1987年出生,博士,副教授,硕士研究生导师。主要研究方向为风电大数据挖掘和结构可靠性优化设计。E-mail:xnxzh501@hnust.edu.cn
基金资助:
国家自然科学基金(51905165,51875199)、河北省自然科学基金创新群体项目(E2020202142)和国家重点研发计划“国家质量基础设施体系”专项(2022YFF0608702)。

Clustering Method of High-dimensional Time Series SCADA Data from Wind Turbines Based on Operational Laws and TICC Algorithm

XIAO Zhao¹, DENG Jiewen¹, LIU Xiaoming², DUAN Shuyong², XU Shouliang³

1. School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201;
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401;
3. Huadian Zhengzhou Mechanical Design Institute Co., Ltd., Zhengzhou 450046

Received:2022-03-07 Revised:2022-10-10 Online:2022-12-05 Published:2023-02-08

摘要/Abstract

摘要： 针对大型风力发电机组高维SCADA时序数据的工况识别问题，结合风电机组运行规律和TICC算法，提出一种自动分割聚类方法。从高维的SCADA数据中选取风速、转速和桨距角等少量特定参数作为初始分割聚类对象，分析特定参数的运行规律，确定风电机组理论的运行工况。选取一段特定参数的历史数据，利用TICC算法进行离线聚类分割，获得聚类的最优特征参数。将最优特征参数作为TICC算法的输入，对新的特定参数时间序列数据进行分类。最后根据特定参数时间序列的聚类结果，对未进行分割的SCADA时序数据进行聚类处理。选取某2.5 MW双馈风电机组的SCADA时间序列数据对方法进行验证，同时将所提出的方法与FCM算法、GMM算法、K-Means算法进行对比研究。实例验证和对比研究表明，所提的聚类方法充分融合理论知识和TICC算法的优点，可高效处理高维SCADA聚类分割问题，同时保证聚类结果与理论分析结果一致性。

关键词: 风电机组, SCADA数据, TICC算法, 时间序列聚类

Abstract: Aiming at the working condition identification of high-dimensional SCADA time series data from wind turbines, a new time series clustering method is proposed by combining the operational laws of wind turbines and the TICC algorithm. A small set of key parameters such as wind velocity, rotational speed and pitch angle are selected from the high-dimensional SCADA data as the cluster datasets. The repeated patterns in temporal data of the key parameters and the theoretical operational conditions of wind turbines are analyzed. Some historical data of key parameters are used to obtain the optimal feature parameters by the TICC algorithm. The optimal feature parameters are then taken as the input of the TICC algorithm which clusters new time series data of the key parameters. Finally, the SCADA time series data of non-key parameters are segmented and clustered based on the clustering results of the key parameters. The SCADA data of 2.5 MW doubly-fed induction generator wind turbine are used to validate the present method. Performance comparison of TICC algorithm, FCM algorithm, GMM algorithm and K-Means algorithm with respect to SCADA data cluster are analyzed. The validation example and comparative study show that the proposed method takes full advantage of the theoretical knowledge and the TICC algorithm. It can efficiently segment and cluster high-dimensional time series SCADA data while the clustering results are consistent with the theoretical results.

Key words: wind turbine, SCADA data, TICC algorithm, time series clustering

中图分类号:

TM614

肖钊, 邓杰文, 刘晓明, 段书用, 许守亮. 基于运行规律和TICC算法的风电SCADA高维时序数据聚类方法[J]. 机械工程学报, 2022, 58(23): 196-207.

XIAO Zhao, DENG Jiewen, LIU Xiaoming, DUAN Shuyong, XU Shouliang. Clustering Method of High-dimensional Time Series SCADA Data from Wind Turbines Based on Operational Laws and TICC Algorithm[J]. Journal of Mechanical Engineering, 2022, 58(23): 196-207.

参考文献

[1] ENEVOLDSEN P,JACOBSON M Z. Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide[J]. Energy for Sustainable Development,2021,60:40-51.
[2] 秦海岩. "十四五",大力发展风电正当时[J]. 风能,2021,11:1-2. QIN Haiyan. A good opportunity to develop wind energy during the 14th Five-Year Plan period[J]. Wind Energy,2021,11:1-2.
[3] 金晓航,孙毅,单继宏,等. 风力发电机组故障诊断与预测技术研究综述[J]. 仪器仪表学报,2017,385:1041-1053. JIN Xiaohang,SUN Yi,SHAN Jihong,et al. Review of wind turbine fault diagnosis and prediction technology research[J]. Chinese Journal of Scientific Instrument,2017,385:1041-1053.
[4] 张帆,刘德顺,戴巨川,等. 一种基于SCADA参数关系的风电机组运行状态识别方法[J]. 机械工程学报,2019,55(4):1-9. ZHANG Fan,LIU Deshun,DAI Juchuan,et al. A method for identifying the operating state of wind turbines based on SCADA parameter relationship[J]. Journal of Mechanical Engineering,2019,55(4):1-9.
[5] CHEN Hansi,LIU Hang,CHU Xuening,et al. Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network[J]. Renewable Energy,2021,1721:1.
[6] 董兴辉,李佳,高迪,等. 风电机组效能和性能统一评估模型研究[J]. 机械工程学报,2021,57(14):253-260. DONG Xinghui,LI Jia,GAO Di,et al. Research on unified assessment model of wind turbine efficiency and performance[J]. Journal of Mechanical Engineering,2021,57(14):253-260.
[7] 刘帅,刘长良,甄成刚. 基于数据分类重建的风电机组故障预警方法[J]. 仪器仪表学报,2019,40(8):1-11. LIU Shuai,LIU Changliang,ZHEN Chenggang. A fault early warning method for wind turbines based on data classification and reconstruction[J]. Chinese Journal of Scientific Instrument,2019,40(8):1-11.
[8] PANG Yanhua,HE Qun,JIANG Guoqian,et al. Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data[J]. Renewable Energy,2020,161:510-524.
[9] LI Yanting,LIU Shujun. Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data[J]. Renewable Energy,2018,134:1.
[10] YANG Wenxian,COURT R,JIANG Jiesheng. Wind turbine condition monitoring by the approach of SCADA data analysis[J]. Renewable Energy,2013,539:365-376.
[11] 郭慧东,王玮,夏明超. 基于数据挖掘的风电机组变桨系统劣化状态在线辨识方法[J]. 中国电机工程学报,2016,369:9. GUO Huidong,WANG Wei,XIA Mingchao. On-line identification method of wind turbine pitch system degradation state based on data mining[J]. Chinese Journal of Electrical Engineering,2016,369:9.
[12] 董玉亮,李亚琼,曹海斌,等. 基于运行工况辨识的风电机组健康状态实时评价方法[J]. 中国电机工程学报,2013,33(11):88-95. DONG Yuliang,LI Yaqiong,CAO Haibin,et al. Real-time evaluation method of wind turbine health status based on operating condition identification[J]. Chinese Journal of Electrical Engineering,2013,33(11):88-95.
[13] LAPIRA E,BRISSET D,ARDAKANI H D,et al. Wind turbine performance assessment using multi-regime modeling approach[J]. Renewable Energy,2012,45:86-95.
[14] JIA Xiaodong,CHAO Jin,BUZZA M,et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves[J]. Renewable Energy,2016,99:1191-1201.
[15] XIAO Zhao,ZHAO Qiancheng,YANG Xuebing,et al. A power performance online assessment method of a wind turbine based on the probabilistic area metric[J]. Applied Sciences,2020,10:1.
[16] ZHU Anfeng,XIAO Zhao,ZHAO Qiancheng. Power data preprocessing method of mountain wind farm based on POT-DBSCAN[J]. Energy Engineering,2021,118(3):549-563.
[17] BECKER E,POSTE P. Keeping the blades turning:Condition monitoring of wind turbine gears[J]. Wind Energy,2006,7(2):26-32.
[18] 刘永前,王飞,时文刚,等. 基于支持向量机的风电机组运行工况分类方法[J]. 太阳能学报,2010,31(9):1191-1197. LIU Yongqian,WANG Fei,SHI Wengang,et al. Operation condition classification method for wind turbine based on support vector machine[J]. Acta Energiae Solaris Sinica,2010,31(9):1191-1197.
[19] HIMBERG J,KORPIAHO K,MANNILA H,et al. Time series segmentation for context recognition in mobile devices[C]//IEEE International Conference on Data Mining. IEEE,2002:1.
[20] BEGUM N,ULANOVA L,WANG Jun,et al. Accelerating dynamic time warping clustering with a novel admissible pruning strategy[C]//ACM Sigkdd International Conference on Knowledge Discovery & Data Mining. ACM,2015:49-58.
[21] HALLAC D,NYSTRUP P,BOYD S. Greedy gaussian segmentation of multivariate time series[J]. Advances in Data Analysis and Classification,2016,13:727-751.
[22] HALLAC D,VARE S,BOYD S,et al. Toeplitz inverse covariance-based clustering of multivariate time series data[C]//The 23rd ACM SIGKDD International Conference. ACM,2017:1.

基于运行规律和TICC算法的风电SCADA高维时序数据聚类方法

Clustering Method of High-dimensional Time Series SCADA Data from Wind Turbines Based on Operational Laws and TICC Algorithm

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

本文评价

[1]	朱永超, 朱才朝, 谭建军, 冉峯, 宋朝省. 考虑数据特征差异的风电齿轮箱群组健康状态预测研究[J]. 机械工程学报, 2024, 60(18): 64-75.
[2]	余晓霞, 汤宝平, 王伟影, 吴宣勇, 李彪. 复杂工况条件下多头注意力双向长短时记忆网络的风电机组缺失数据修复方法研究[J]. 机械工程学报, 2023, 59(14): 1-9.
[3]	张晓英, 吴宏强, 舒子江, 王琨, 陈伟. 基于PCA和改进ABC-K聚类算法的双馈机组风电场等值建模^*[J]. 电气工程学报, 2023, 18(1): 201-210.
[4]	谭建军, 朱才朝, 李浩, 宋朝省, 董晔弘. 基础运动对漂浮式风电机组齿轮箱传动系统附加激励的影响[J]. 机械工程学报, 2023, 59(1): 35-49.
[5]	王昊, 郝万君, 李泽, 崔国增, 吴宇, 张正夫, 孙志辉. 基于滑模观测器的风力机故障快速重构方法设计[J]. 机械工程学报, 2021, 57(14): 261-269,281.
[6]	董兴辉, 李佳, 高迪, 郑凯. 风电机组效能和性能统一评估模型研究[J]. 机械工程学报, 2021, 57(14): 253-260.
[7]	党存禄, 李旭鹏, 孙海斌, 武文成. 双馈风电机组次同步控制相互作用的抑制方法 ^*[J]. 电气工程学报, 2021, 16(1): 119-126.
[8]	张帆, 刘德顺, 戴巨川, 王超, 沈祥兵. 一种基于SCADA参数关系的风电机组运行状态识别方法[J]. 机械工程学报, 2019, 55(4): 1-9.
[9]	包广清,李媛,马明,汪宁渤. 减载运行方式下直驱永磁风电机组虚拟同步控制策略 ^*[J]. 电气工程学报, 2019, 14(3): 47-53.
[10]	陈明,陈永军,李力森. 变频柜在风电机组塔底工作的散热研究[J]. 电气工程学报, 2018, 13(5): 33-39.
[11]	董兴辉, 张鑫淼, 张光, 王帅. 基于云模型的风电机组输出功率特性分析[J]. 机械工程学报, 2017, 53(22): 198-205.
[12]	沈小军,周冲成,吕洪. 大型风电场风电机组分组方法研究综述[J]. 电气工程学报, 2016, 11(5): 1-10.
[13]	戴巨川. 叶片断裂事故条件下直驱式风电机组动态特性分析[J]. , 2013, 49(2): 190-198.
[14]	郭建英;孙永全;王铭义;丁喜波. 基于计算机仿真的风电机组系统可靠性综合[J]. , 2012, 48(2): 2-8.
[15]	应有;许国东. 基于载荷优化的风电机组变桨控制技术研究[J]. , 2011, 47(16): 106-111.