Wind Turbines Operating State Identification Method Based on Transfer Component Analysis

Linyan LI; Shuang HAN; Yajie ZHANG; Yang CHEN; Li LI; Zhiqiang PAN

doi:10.16513/j.2096-2185.DE.2207102

TSINGHUA JOURNALS HOME

Source Journal for Chinese Scientific and Technical Papers and Citations

RCCSE Chinese Core Academic Journals

Classification Catalogue of High Quality Scientific Journals in the field of Energy and Power

Classification Catalogue of High Quality Scientific Journals in Coal Field

PDF(2569 KB)

Distributed Energy ›› 2022, Vol. 7 ›› Issue (1) : 12-19. DOI: 10.16513/j.2096-2185.DE.2207102

Basic Research

Wind Turbines Operating State Identification Method Based on Transfer Component Analysis

Linyan LI ¹^,² ,
Shuang HAN ¹^,² ,
Yajie ZHANG ¹^,² ,
Yang CHEN ¹^,² ,
Li LI ¹^,² ,
Zhiqiang PAN ³

Author information +

History +

HeighLight

The identification of the operating state of wind turbines is of great significance to the performance evaluation of wind turbines and the refined management of wind farms. However, the supervisory control and data acquisition (SCADA) data distribution of different wind turbines varies significantly. If the trained normal behavior model of single typhoon turbine is directly applied to the operating state identification of multiple wind turbines, the identification accuracy is low. In order to improve the recognition accuracy, it is necessary to conduct repetitive training for each wind turbine normal behavior model, which is a large workload.Therefore, a multi-wind turbines operating state identification model based on transfer component analysis (TCA) is proposed. Firstly, a variable optimization method based on the maximal information coefficient and back propagation (BP) double hidden layer neural network is used to mine the key influencing variables of wind turbine operating status. Then, the normal behavior model of wind turbine based on BP double-hidden layer neural network is constructed with the optimal variables as input and the power as output. Finally, based on transfer component analysis, a multi-wind turbines operation state classification model is constructed. The results show that the proposed model can solve the problem of data distribution differences between different wind turbines and improve the accuracy and efficiency of the operating state partition model.

Key words

wind turbine / operating state / normal behavior model / transfer learning / machine learning

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Linyan LI , Shuang HAN , Yajie ZHANG , et al . Wind Turbines Operating State Identification Method Based on Transfer Component Analysis[J]. Distributed Energy Resources. 2022, 7(1): 12-19 https://doi.org/10.16513/j.2096-2185.DE.2207102

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	郑若楠. 数据驱动的风电机组叶片结冰在线检测方法[J]. 分布式能源，2019, 4(1): 1-7. ZHENG Ruonan. On-line detection method of ice on wind turbine blade driven by data[J]. Distributed Energy, 2019, 4(1): 1-7. Cited in this article [1]

[2]	张真真，吴立东，陈晓敏，等. 基于支持向量机的风电机组变桨系统故障诊断[J]. 分布式能源，2021, 6(3): 70-75. ZHANG Zhenzhen, WU Lidong, CHEN Xiaomin, et al. Fault diagnosis of windturbine pitch system based on support vector machine[J]. Distributed Energy, 2021, 6(3): 70-75. Cited in this article [1]

[3]	ZHAI Yongjie, WANG Dongfeng, ZHANG Junying, et al. Research on early fault diagnostic method of wind turbines[J]. Telkomnika Indonesian Journal of Electrical Engineering, 2013, 11(5): 2330-2341. Cited in this article [1]

[4]	LI Jian, LEI Xiao, LI Hui, et al. Normal behavior models for the condition assessment of wind turbine generator systems[J]. Electric Power Components and Systems, 2014, 42(11): 1201-1212. Cited in this article [1]

[5]	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): 9. ZHANG Fan, LIU Deshun, DAI Juchuan, et al. An operating condition recognition method of wind turbine based on SCADA parameter relations[J]. Journal of Mechanical Engineering, 2019, 55(4): 9. Cited in this article [1]

[6]	CHENG Yi, HU Yang, SONG Ziqiu. Fuzzy comprehensive evaluation of wind turbine operation condition based on weights calculation via factor analysis[C]//2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2019. Cited in this article [1]

[7]	HE Chengbing, MA Li, WANG Runze, et al. Condition evaluation method of wind turbine based on real-time reliability[C]//2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). 2019: 167-172. Cited in this article [1]

[8]	XU Chun, CHEN Yang, LIU Yongqian, et al. Wind turbine operation condition identification method based on variable optimization selection and machine learning[C]//The 10th Renewable Power Generation Conference (RPG 2021), 2021: 1128-1133. Cited in this article [1]

[9]

杨

毅

，范

栋琛

，殷

浩然

，等. 基于深度迁移学习的输电线路故障选相模型及其可迁移性研究[J]. 电力自动化设备，2020, 40(10): 165-172.

YANG

, FAN

Dongchen

, YIN

Haoran

, et al. Transmission line fault phase selection model based on deep-transfer learning and its transferability[J]. Electric Power Automation Equipment, 2020, 40(10): 165-172.

Cited in this article [1]

[10]	REN Chao, XU Yan. Transfer learning-based power system online dynamic security assessment: using one model to assess many unlearned faults[J]. IEEE Transactions on Power Systems, 2019, PP(99): 1-1. Cited in this article [1]

[11]	兰雨涛，胡超凡，金京，等. 基于迁移成分分析的跨域轴承故障分类方法研究[J]. 机电工程，2021, 38(5): 7. LAN Yutao, HU Chaofan, JIN Jing, et al. Cross domain classification method of bearings fault based on transfer component analysis[J]. Journal of Mechanical & Electrical Engineering, 2021, 38(5): 7. Cited in this article [1]

[12]	RESHEF D N, RESHEF Y A, FINUCANE H K, et al. Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524. Cited in this article [1]

[13]	韩爽，孟航，刘永前，等. 增量处理双隐层BP神经网络风电功率预测模型[J]. 太阳能学报，2015, 36(9): 2238-2244. HAN Shuang, MENG Hang, LIU Yongqian, et al. Study on optimization of BP neural network wind power prediction model with two hidden layers[J]. Acta Energiae Solaris Sinica, 2015, 36(9): 2238-2244. Cited in this article [1]

[14]	乔延辉. 计及风速时空变化规律的风电功率预测方法研究[D]. 北京：华北电力大学，2019. QIAO Yanhui. Research on wind power prediction based on wind speed temporal and spatial variation[D]. Beijing: North China Electric Power University, 2019. Cited in this article [1]

[15]	金鑫城，杨秀媛. 基于失真数据降噪的数据预处理方法及其在风电功率预测中的应用[J]. 发电技术，2020, 41(4): 447-451. JIN Xincheng, YANG Xiuyuan. Data pre-processing method based on distorted data noise reduction and its application in wind power prediction[J]. Power Generation Technology, 2020, 41(4): 447-451. Cited in this article [1]

[16]	魏乐，李思莹. 基于MEEMD-LSSVM的风电功率超短期预测研究[J]. 智慧电力，2020, 48(5): 21-26. WEI Le, LI Siying. Ultra-short-term forecast for wind power based on MEEMD-LSSV[J]. Smart Power, 2020, 48(5): 21-26. Cited in this article [1]

[17]	PAN S J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2): 199-210. Cited in this article [1]