DATA CLEANING METHOD CONSIDERING TEMPORAL AND SPATIAL CORRELATION FOR MEASURED WIND SPEED OF WIND TURBINES

Li Li; Liang Yuan; Lin Na; Yan Jie; Meng Hang; Liu Yongqian

doi:10.19912/j.0254-0096.tynxb.2023-0201

PDF(5636 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (6) : 461-469. DOI: 10.19912/j.0254-0096.tynxb.2023-0201

DATA CLEANING METHOD CONSIDERING TEMPORAL AND SPATIAL CORRELATION FOR MEASURED WIND SPEED OF WIND TURBINES

Li Li^1,2, Liang Yuan^1,2, Lin Na^1,2, Yan Jie^1,2, Meng Hang^1,2, Liu Yongqian^1,2

Author information +

History +

Abstract

To obtain reliable and accurate wind speed data, a data cleaning method for measured wind speed of wind turbines was proposed in this study. The method incorporates spatiotemporal correlation by utilizing a graph convolutional neural network （GCN） to extract spatial correlation information and a bidirectional long short-term memory neural network （Bi-LSTM） to extract temporal correlation information. A GCN-LSTM model was established to reconstruct the wind speed of each wind turbine, so as to realize identification and removal of abnormal wind speed. The study also analyzes the spatiotemporal characteristics of wind speed and their impact on the accuracy of the proposed model. Two important modeling parameters are identified： the optimal time scale and the number of wind turbines. The proposed method was validated by using data from four wind farms with different terrains in China. The results show that incorporating spatiotemporal correlation can effectively improve accuracy of data cleaning. Moreover, the higher the spatiotemporal correlation of wind speed, the smaller the cleaning error. The proposed model has robustness in cleaning wind speed data under various terrain types.

Key words

wind farm / wind turbines / graph neural networks / long short-term memory / spatiotemporal correlation of wind speed / data cleaning

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Li Li, Liang Yuan, Lin Na, Yan Jie, Meng Hang, Liu Yongqian. DATA CLEANING METHOD CONSIDERING TEMPORAL AND SPATIAL CORRELATION FOR MEASURED WIND SPEED OF WIND TURBINES[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 461-469 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0201

References

[1] WANG Z J, WANG L, HUANG C.A fast abnormal data cleaning algorithm for performance evaluation of wind turbine[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 1-12.
[2] CHEN H S, LIU H, CHU X N, et al.Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network[J]. Renewable energy, 2021, 172: 829-840.
[3] 赵洪山, 刘辉海, 刘宏杨, 等. 基于堆叠自编码网络的风电机组发电机状态监测与故障诊断[J]. 电力系统自动化, 2018, 42(11): 102-108.
ZHAO H S, LIU H H, LIU H Y, et al.Condition monitoring and fault diagnosis of wind turbine generator based on stacked autoencoder network[J]. Automation of electric power systems, 2018, 42(11): 102-108.
[4] 董海鹰, 陈彦求, 汪宁渤, 等. 含前端调速式风电机组风电场的无功运行优化[J]. 太阳能学报, 2020, 41(11): 254-260.
DONG H Y, CHEN Y Q, WANG N B, et al.Optimizatton in reactive power operation of wind farm with front-end speed controlled wind turbine[J]. Acta energiae solaris sinica, 2020, 41(11): 254-260.
[5] 张晋华, 吴文静, 王卓然, 等. 基于降低风电场损耗的风电场优化调度研究[J]. 太阳能学报, 2018, 39(4): 1085-1096.
ZHANG J H, WU W J, WANG Z R, et al.Research of wind farm optimal scheduling based on reducing wind farm losses[J]. Acta energiae solaris sinica, 2018, 39(4): 1085-1096.
[6] 张浩田, 温蜜, 李晋国, 等. 数据驱动的时间注意力卷积风电功率预测模型[J]. 太阳能学报, 2022, 43(10): 167-176.
ZHANG H T, WEN M, LI J G, et al.Data driven time attention convolution wind power prediction model[J]. Acta energiae solaris sinica, 2022, 43(10): 167-176.
[7] ASLAM M, KIM J S, JUNG J.Multi-step ahead wind power forecasting based on dual-attention mechanism[J]. Energy reports, 2023, 9: 239-251.
[8] 阎洁, 许成志, 刘永前, 等. 基于风速云模型相似日的短期风电功率预测方法[J]. 电力系统自动化, 2018, 42(6): 53-59.
YAN J, XU C Z, LIU Y Q, et al.Short-term wind power prediction method based on wind speed cloud model in similar day[J]. Automation of electric power systems, 2018, 42(6): 53-59.
[9] 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 X J, FU X J, ZHOU C C, 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.
[10] 赵永宁, 叶林, 朱倩雯. 风电场弃风异常数据簇的特征及处理方法[J]. 电力系统自动化, 2014, 38(21): 39-46.
ZHAO Y N, YE L, ZHU Q W.Characteristics and processing method of abnormal data clusters caused by wind curtailments in wind farms[J]. Automation of electric power systems, 2014, 38(21): 39-46.
[11] SHEN X J, FU X J, ZHOU C C.A combined algorithm for cleaning abnormal data of wind turbine power curve based on change point grouping algorithm and quartile algorithm[J]. IEEE transactions on sustainable energy, 2019, 10(1): 46-54.
[12] 杨茂, 杨春霖, 杨琼琼, 等. 计及风向信息的风电功率异常数据识别研究[J]. 太阳能学报, 2019, 40(11): 3265-3272.
YANG M, YANG C L, YANG Q Q, et al.Study on data recognition of wind power abnormality considering wind direction information[J]. Acta energiae solaris sinica, 2019, 40(11): 3265-3272.
[13] 梅勇, 李霄, 胡在春, 等. 基于风电机组控制原理的风功率数据识别与清洗方法[J]. 动力工程学报, 2021, 41(4): 316-322, 329.
MEI Y, LI X, HU Z C, et al.Identification and cleaning of wind power data methods based on control principle of wind turbine generator system[J]. Journal of Chinese Society of Power Engineering, 2021, 41(4): 316-322, 329.
[14] 邹同华, 高云鹏, 伊慧娟, 等. 基于Thompson tau-四分位和多点插值的风电功率异常数据处理[J]. 电力系统自动化, 2020, 44(15): 156-162.
ZOU T H, GAO Y P, YI H J, et al.Processing of wind power abnormal data based on Thompson tau-quartile and multi-point interpolation[J]. Automation of electric power systems, 2020, 44(15): 156-162.
[15] 张东英, 李伟花, 刘燕华, 等. 风电场有功功率异常运行数据重构方法[J]. 电力系统自动化, 2014, 38(5): 14-18, 24.
ZHANG D Y, LI W H, LIU Y H, et al.Reconstruction method of active power historical operating data for wind farm[J]. Automation of electric power systems, 2014, 38(5): 14-18, 24.
[16] 沈小军, 周冲成, 吕洪. 基于运行数据的风电机组间风速相关性统计分析[J]. 电工技术学报, 2017, 32(16): 265-274.
SHEN X J, ZHOU C C, LYU H.Statistical analysis of wind speed correlation between wind turbines based on operational data[J]. Transactions of China Electrotechnical Society, 2017, 32(16): 265-274.
[17] 李丹, 甘月琳, 缪书唯, 等. 计及时间演变和空间相关的多风电场短期功率预测[J]. 电网技术, 2023, 47(3): 1117-1128.
LI D, GAN Y L, MIAO S W, et al.Short-term power prediction for multiple wind farms considering temporal evolution and spatial correlation[J]. Power system technology, 2023, 47(3): 1117-1128.
[18] 纪德洋, 金锋, 冬雷, 等. 基于皮尔逊相关系数的光伏电站数据修复[J]. 中国电机工程学报, 2022, 42(4): 1514-1523.
JI D Y, JIN F, DONG L, et al.Data repairing of photovoltaic power plant based on Pearson correlation coefficient[J]. Proceedings of the CSEE, 2022, 42(4): 1514-1523.
[19] 邱明, 鲁冠军, 吴昊天, 等. 基于数据清洗与组合学习的光伏发电功率预测方法研究[J]. 可再生能源, 2020, 38(12): 1583-1589.
QIU M, LU G J, WU H T, et al.Short-term photovoltaic forecasting based on data cleansing and model combination[J]. Renewable energy resources, 2020, 38(12): 1583-1589.