采用滑动窗口及多重加噪比堆栈降噪自编码的风电机组状态异常检测方法

doi:10.19595/j.cnki.1000-6753.tces.181577

Abstract
Figure/Table
References (32)
Related Citation (15)

Download: PDF (41394 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

This paper presents a multivariable reconstruction approach to detect the anomaly conditions of wind turbines (WTs), focusing on the data collected from the wind farm supervisory control and data acquisition (SCADA) system. Firstly, the stacked denoising autoencoders (SDAE) model with sliding window was developed to capture nonlinear correlations among multiple variables and temporal dependencies at each variable simultaneously, and reconstruct the status data of WTs. Secondly, the SDAE model with sliding window was trained under multiple noise ratios to learn the coarse-grained and fine-grained features of condition parameters for improving the feature representation of model. Finally, the Mahalanobis distance (MD) of reconstruction error was defined as the monitoring index. The threshold of monitoring index was obtained based on the probability density function (PDF) of MD with the kernel density estimation. The duration of over-limit was utilized to detect the anomaly conditions of WTs. The anomaly conditions of WTs were identified based on the contribution of each variable. The analysis results of SCADA data collected from a wind farm in Eastern China show that the proposed method is effective for the anomaly condition detection of actual wind turbines.

Key words： Wind turbine anomaly detection supervisory control and data acquisition system stacked denoising autoencoders sliding window multiple noise ratios

Received: 08 October 2018 Published: 17 January 2020

PACS:

TM315

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Chen Junsheng
	Li Jian
	Chen Weigen
	Sun Peng

Cite this article:

Chen Junsheng,Li Jian,Chen Weigen等. A Method for Detecting Anomaly Conditions of Wind Turbines Using Stacked Denoising Autoencoders with Sliding Window and Multiple Noise Ratios[J]. Transactions of China Electrotechnical Society, 2020, 35(2): 346-358.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181577 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I2/346

[1] Wei Qiao, Lu Dingguo.A survey on wind turbine condition monitoring and fault diagnosis—part I: components and subsystems[J]. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6546-6557.
[2] Milborrow D.Operation and maintenance costs compared and revealed[J]. Wind Newsletter, 2006, 19(3): 1-3.
[3] 杭俊, 张建忠, 程明, 等. 风力发电系统状态监测和故障诊断技术综述[J]. 电工技术学报, 2013, 28(4): 262-271.
Hang Jun, Zhang Jianzhong, Cheng Ming, et al.An overview of condition monitoring and fault diagnostic for wind energy conversion system[J]. Transactions of China Electrotechnical Society, 2013, 28(4): 261-271.
[4] Hameed Z, Hong Y S, Cho Y M, et al.Condition monitoring and fault detection of wind turbines and related algorithms: a review[J]. Renewable & Sustainable Energy Reviews, 2009, 13(1): 1-39.
[5] Wei X, Verhaegen M, Engelen T V.Sensor fault detection and isolation for wind turbines based on subspace identification and kalman filter techni- ques[J]. International Journal of Adaptive Control and Signal Processing, 2010, 24(8): 687-707.
[6] Sanchez H, Escobet T, Puig V, et al.Fault diagnosis of an advanced wind turbine benchmark using interval- based arrs and observers[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3783-3793.
[7] Chan C W, Song Hua, Zhang Hongyue.Application of fully decoupled parity equation in fault detection and identification of DC motors[J]. IEEE Transa- ctions on Industrial Electronics, 2006, 53(4): 1277-1284.
[8] Caselitz P, Bussel G W, Spinato F.Rotor condition monitoring for improved operational safety of offshore wind energy converters[J]. Journal of Solar Energy Engineering, 2005, 127(2): 253-261.
[9] 李东东, 周文磊, 郑小霞, 等. 基于自适应EEMD和分层分形维数的风电机组行星齿轮箱故障检测[J]. 电工技术学报, 2017, 32(22): 233-241.
Li Dongdong, Zhou Wenlei, Zheng Xiaoxia, et al.Diagnosis of wind turbine planetary gearbox faults based on adaptive EEMD and hierarchical fractal dimension[J]. Transactions of China Electrotechnical Society, 2017, 32(22): 233-241.
[10] 杨明, 董传洋, 徐殿国. 基于电机驱动系统的齿轮故障诊断方法综述[J]. 电工技术学报, 2016, 31(4): 132-140.
Yang Ming, Dong Chuanyang, Xu Dianguo.Review of gear fault diagnosis methods based on motor drive system[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 132-140.
[11] Soua S, Lieshout P V, Perera A, et al.Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring[J]. Renewable Energy, 2013, 51(2): 175-181.
[12] 沈小军, 付雪姣, 周冲成, 等. 风电机组风速-功率异常运行数据特征及清洗方法[J]. 电工技术学报, 2018, 33(14): 3353-3361.
Shen Xiaojun, Fu Xuejiao, Zhou Chongcheng, et al.Characteristics of outliers in wind speed-power operation data of wind turbines and its cleaning method[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3353-3361.
[13] Cross P, Ma X D.Nonlinear system identification for model-based condition monitoring of wind turbines[J]. Renewable Energy, 2014, 71(11): 166-175.
[14] Yampikulsakul N, Byon E, Huang S, et al.Condition monitoring of wind power system with nonparametric regression analysis[J]. IEEE Transactions on Energy Conversion, 2014, 29(2): 288-299.
[15] Sun Peng, Li Jian, Wang Caisheng, et al.A generalized model for wind turbine anomaly identification based on SCADA data[J]. Applied Energy, 2016, 168: 550-567.
[16] Skrimpas G A, Sweeney C W, Marhadi K S, et al.Employment of kernel methods on wind turbine power performance assessment[J]. IEEE Transactions on Sustainable Energy, 2015, 6(3): 698-706.
[17] Kusiak A, Verma A.A data-mining approach to monitoring wind turbines[J]. IEEE Transactions on Sustainable Energy, 2011, 3(1): 150-157.
[18] Bangalore P, Tjernberg L B.An artificial neural network approach for early fault detection of gearbox bearings[J]. IEEE Transactions on Smart Grid, 2017, 6(2): 980-987.
[19] Zaher A, Mcarthur S D J, Infield D G, et al. Online wind turbine fault detection through automated SCADA data analysis[J]. Wind Energy, 2010, 12(6): 574-593.
[20] 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 Shi, Yu Zhongyuan, Meng Kaifeng, 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.
[21] Peng G, Infield D, Yang X.Wind turbine generator condition-monitoring using temperature trend analysis[J]. IEEE Transactions on Sustainable Energy, 2011, 3(1): 124-133.
[22] Schlechtingen M, Santos I F, Achiche S.Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description[J]. Applied Soft Computing, 2013, 13(1): 259-270.
[23] Kruger M, Ding S X, Haghani A, et al.A data-driven approach for fault diagnosis in gearbox of wind energy conversion system[C]//Control and Fault- Tolerant Systems, Nice, France, 2014: 359-364.
[24] Wang Long, Zhang Zijun, Xu Jia, et al.Wind turbine blade breakage monitoring with deep autoencoders[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 2824-2833.
[25] 刘辉海, 赵星宇, 赵洪山, 等. 基于深度自编码网络模型的风电机组齿轮箱故障检测[J]. 电工技术学报, 2017, 32(17): 156-163.
Liu Huihai, Zhao Xingyu, Zhao Hongshan, et al.Fault detection of wind turbine gearbox based on deep autoencoder network[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 156-163.
[26] Vincent P, Larochelle H, Bengio Y, et al.Extracting and composing robust features with denoising auto- encoders[C]//International Conference on Machine Learning, Helsinki, 2008: 1096-1103.
[27] 代杰杰, 宋辉, 杨祎, 等. 基于栈式降噪自编码器的输变电设备状态数据清洗方法[J]. 电力系统自动化, 2017, 41(12): 224-230.
Dai Jiejie, Song Hui, Yang Yi, et al.Cleaning method of status data of power transmission and trans- formation equipment based on stacked denoising autoencoders[J]. Automation of Electric Power Systems, 2017, 41(12): 224-230.
[28] Ku W, Storer R H, Georgakis C.Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory systems, 1995, 30(1): 179-196.
[29] Vincent P, Larchelle H, Lajoie I, et al.Stacked denoising autoencoders: Learning useful representa- tions in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12): 3371-3408.
[30] Chandra B, Sharma R K.Adaptive noise schedule for denoising autoencoder[C]//Proceedings of the 21st International Conference on Neural Information, Kuching, Malaysia, 2014: 535-542.
[31] Geras K J, Sutton C.Scheduled denoising auto- encoders[C]//International Conference on Learning Representations, San Diego, USA, 2015: 1-11.
[32] Wand M P, Jones M C.Kernel smoothing[M]. London: Chapman and Hall, 1995.