基于速度-关联约束的风电机组风速感知异常数据识别方法

doi:10.19595/j.cnki.1000-6753.tces.211893

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2166 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The wind speed sensors of wind turbines, due to long-term exposure to the harsh operating environment, are prone to instability or misalignment, resulting in a large amount of abnormal data in the collected raw data. The existing data cleaning methods for wind turbines, which primarily focus on the mapping relations between wind speed and wind power in power curves, are designed for wind power generation performance evaluation or wind power curve fitting. Few anomaly detection methods of wind speed data are proposed for the performance evaluation and health management (PEHM) for wind speed sensors. Consider the fact that different applications have different boundaries and requirements for data cleansing, this paper proposes a wind speed data cleaning method based on temporal and spatial correlation characteristics to deeply mine various abnormal data, especially the weak fault data located in or around the wind power curve. The anomaly detection results are helpful to find more information of wind speed sensers behaviors, which can be furtherly employed for PEHM.
Firstly, the typical abnormal wind speed data in wind farms and their temporal features are summarized, which are classified into the sudden-changes and gradual-changes wind speed outliers. Then, a combined method using velocity and correlation constraints is proposed to detect various outliers. Since the local features of wind speed data are prone to partial coverage and annihilation when using long time scale data to model their temporal relations and correlations, this paper proposes a change-points detection algorithm based on shape binary segmentation to perform temporal interval partitioning for the global wind speed series. Thus, the velocity and correlation constraints are constructed for each sub-series in temporal and spatial dimensions to conduct data cleaning tasks.
The experiment results on two datasets, including a simulated dataset and an actual wind farm dataset, show that, the proposed method is capable of detecting various abnormal wind speed data for wind turbines. The precision rates of the proposed anomaly detection method for sudden-changes and gradual-changes abnormal wind speed data reach 95.57% and 100%, respectively. Statistics on the abnormal data indicate that the gradual-changes abnormal wind speed data holds the dominate proportion among the detected outliers and the rate of abnormal data reaches 21.87%. An explanation for that is the wind capture performance of the selected sensor decrease severely, instead, the anti-electromagnetic performance is perfect. Interestingly, there are many scatters inside the wind speed and power curve that are detected as abnormal wind speed data by the proposed method. However, such scatters are detected as normal data for power curves fitting and wind power generation performance evaluation, which furtherly proves that the boundaries and definitions for outliers vary for different data category and digital applications.
The following conclusions can be drawn from the case analysis: (1) The shape binary segmentation-based change-points detection method is capable of effectively identifying the moments when the temporal features of wind speed series change significantly. The temporally segmented local wind speed sequences are mor suitable to correlations modelling with higher accuracy and reliability. (2) The proposed data cleaning method based on velocity and correlation constraints provides accurate detection for various abnormal wind speed data in wind farms, especially for the weak fault data. (3) This paper presents a general data cleaning framework for time series based on spatial and temporal correlation characteristics, which takes the differences of the sensitivity to abnormal data in practical applications into account and can accurately identify various abnormal wind speed data with precision rate of 95.57% and 100% for sudden-changes and gradual-changes outliers.

Key words： Wind turbines wind speed outliers data cleaning shape-based binary segmentation velocity-correlation constraints

Received: 18 November 2021

PACS:

TK83

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Yang
	Shen Xiaojun
	Zhang Yangfan
	Wang Yu

Cite this article:

Li Yang,Shen Xiaojun,Zhang Yangfan等. Cleaning Method of Wind Speed Outliers for Wind Turbines Based on Velocity and Correlation Constraints[J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1793-1807.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211893 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I7/1793

[1] 韩肖清, 李廷钧, 张东霞, 等. 双碳目标下的新型电力系统规划新问题及关键技术[J]. 高电压技术, 2021, 47(9): 3036-3046.
Han Xiaoqing, Li Tingjun, Zhang Dongxia, et al.New issues and key technologies of new power system planning under double carbon goals[J]. High Voltage Engineering, 2021, 47(9): 3036-3046.
[2] 武佳卉, 邵振国, 杨少华, 等. 数据清洗在新能源功率预测中的研究综述和展望[J]. 电气技术, 2020, 21(11): 1-6.
Wu Jiahui, Shao Zhenguo, Yang Shaohua, et al.Review and prospect of data cleaning in renewable energy power prediction[J]. Electrical Engineering, 2020, 21(11): 1-6.
[3] 娄建楼, 胥佳, 单凯. 基于机舱风速计的风电机组功率特性评估方法[J]. 电力系统自动化, 2016, 40(9): 23-28.
Lou Jianlou, Xu Jia, Shan Kai.Power performance measuring method of wind turbines based on nacelle anemometer[J]. Automation of Electric Power Systems, 2016, 40(9): 23-28.
[4] 潘超, 李润宇, 蔡国伟, 等. 基于时空关联分解重构的风速超短期预测[J]. 电工技术学报, 2021, 36(22): 4739-4748.
Pan Chao, Li Runyu, Cai Guowei, et al.Multi-step ultra-short-term wind speed prediction based on decomposition and reconstruction of time-spatial correlation[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4739-4748.
[5] 关中杰, 鲁效平, 李钢强, 等. 基于风速模型的风电机组动态转矩前馈控制技术[J]. 电工技术学报, 2018, 33(22): 5338-5345.
Guan Zhongjie, Lu Xiaoping, Li Gangqiang, et al.Dynamic torque feed forward control technology of wind turbine based on wind speed model[J]. Transactions of China Electrotechnical Society, 2018, 33(22): 5338-5345.
[6] 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(27): 39-46.
Zhao Yongning, Ye Lin, Zhu Qianwen.Characteristics and processing method of abnormal data cluster caused by wind curtailments in wind farms[J]. Automation of Electric Power Systems, 2014, 38(27): 39-46.
[7] 胡阳, 乔依林. 基于置信等效边界模型的风功率数据清洗方法[J]. 电力系统自动化, 2018, 42(15): 18-23.
Hu Yang, Qiao Yilin.Wind power data cleaning method based on confidence equivalent boundary model[J]. Automation of Electric Power Systems, 2018, 42(15): 18-23.
[8] 娄建楼, 胥佳, 陆恒, 等. 基于功率曲线的风电机组数据清洗算法[J]. 电力系统自动化, 2016, 40(10): 116-121.
Lou Jianlou, Xu Jia, Lu Heng, et al.Wind turbine data-cleaning algorithm based on power curve[J]. Automation of Electric Power Systems, 2016, 40(10): 116-121.
[9] Shen Xiaojun, Fu Xuejiao, Zhou Chongcheng.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.
[10] 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 Tonghua, Gao Yunpeng, Yin Huijuan, 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.
[11] Zheng Le, Hu Wei, Min Yong.Raw wind data preprocessing: a data-mining approach[J]. IEEE Transactions on Sustainable Energy, 2014, 6(1): 11-19.
[12] 凡航, 张雪敏, 梅生伟, 等. 基于时空神经网络的风电场超短期风速预测模型[J]. 电力系统自动化, 2021, 45(1): 28-35.
Fan Hang, Zhang Xuemin, Mei Shengwei, et al.Ultra-short-term wind speed prediction model for wind farms based on spatiotemporal neural network[J]. Automation of Electric Power Systems, 2021, 45(1): 28-35.
[13] 马然, 栗文义, 齐咏生. 风电机组健康状态预测中异常数据在线清洗[J]. 电工技术学报, 2021, 36(10): 132-142.
Ma Ran, Li Wenyi, Qi Yongsheng.Online cleaning of abnormal data for the prediction of wind turbine health condition[J]. Transactions of China Electrotechnical Society, 2017, 11(1): 132-142.
[14] 庄丹, 刘友波, 马铁丰. 多变点检测问题的Shape-based BS算法[J]. 高校应用数学学报A刊, 2019, 34(2): 151-164.
Zhuang Dan, Liu Youbo, Ma Tiefeng.Shape-based BS algorithm for multiple change-point detection[J]. Applied Mathematics A Journal of Chinese Universities, 2019, 34(2): 151-164.
[15] 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.
[16] 叶燕飞, 王琦, 陈宁, 等. 考虑时空分布特性的风速预测模型[J]. 电力系统保护与控制, 2017, 45(4): 114-120.
Ye Yanfei, Wang Qi, Chen Ning, et al.Wind forecast model considering the characteristics of temporal and spatial distribution[J]. Power System Protection and Control, 2017, 45(4): 114-120.
[17] 沈小军, 周冲成, 付雪娇. 基于机联网-空间相关性权重的风电机组风速预测研究[J]. 电工技术学报, 2021, 36(9): 1782-1790.
Shen Xiaojun, Zhou Chongcheng, Fu Xuejiao.Wind speed prediction of wind turbine based on the internet of machines and spatial correlation weight[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1782-1790.
[18] Wu Yuxi, Wu Qingbiao, Zhu Jiaqi.Data-driven wind speed forecasting using deep feature extraction and LSTM[J]. IET Renewable Power Generation, 2019, 13(12): 2062-2069.
[19] Piotr F.Wild binary segmentation for multiple change-point detection[J]. Annals of Statistics, 2014, 42(6): 2243-2281.
[20] Zhu Qiaomu, Chen Jinfu, Zhu Lin, et al.Learning temporal and spatial correlations jointly: a unified framework for wind speed prediction[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 509-523.
[21] Li Pai, Guan Xiaohong, Wu Jiang, et al.Modeling dynamic spatial correlations of geographically distributed wind farms and constructing ellipsoidal uncertainty sets for optimization-based generation scheduling[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4):1594-1605.
[22] 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 Xiaojun, Zhou Chongcheng, Lü Hong.Statistical analysis of wind speed correlation between wind turbines based on operational data[J]. Transactions of China Electrotechnical Society, 2017, 32(16): 265-274.
[23] 段偲默, 苗世洪, 霍雪松, 等. 基于动态Copula的风光联合出力建模及动态相关性分析[J]. 电力系统保护与控制, 2019, 47(5): 35-42.
Duan Simo, Miao Shihong, Huo Xuesong, et al.Modeling and dynamic correlation analysis of wind/solar power joint output based on dynamic Copula[J]. Power System Protection and Control, 2019, 47(5): 35-42.
[24] 黎静华,文劲宇,程时杰,等.考虑多风电场出力Copula相关关系的场景生成方法[J]. 中国电机工程学报, 2013, 33(16): 30-36.
Li Jinghua, Wen Jinyu, Cheng Shijie, et al.A scene generation method considering copula correlation relationship of multi-wind farms power[J]. Proceedings of the CSEE, 2013, 33(16): 30-36.