An Operation Quality Evaluation Method and Its Applications for Wind Speed Sensors of Wind Farms Based on Operation Data
Li Yang1, Shen Xiaojun1, Yang Weixin2, Zhang Yangfan2
1. Department of Electrical Engineering Tongji University Shanghai 200092 China; 2. State Grid Jibei Electric Power Research Institute Beijing 100045 China
Abstract Wind speed sensors of wind turbines, especially offshore wind turbines, are prone to suffer from defects due to long-term exposure to complex and harsh environments, which negatively affects the quality of wind speed data. No wind signal or wrong wind signal fed into the unit control system tends to cause false start-stop and pitching actions, endangering the power generation efficiency and operation safety of wind farms. Existing methods focus on the fault diagnosis of sensors, serving as real-time operation control of units. Considering that the sensor may fail due to natural weather events (NWEs), the sensor is likely to recover smooth operation after NWEs. Extracting the operational quality (measurement accuracy, operational stability, disturbance sensitivity, and performance evolution trend) is difficult. Thus, refinement modeling for sensor operating quality helps to analyze its instability, misalignment, and other defects for the full-life cycle healthy management and proactive inspection of wind farms. Firstly, the operational quality characterization indicators and quantification rules for wind speed sensors are constructed from the dimensions of operation reliability, measurement accuracy, anti-disturbance ability, environment features, and inherent property. Then, a comprehensive evaluation model is employed using data envelope analysis (DEA) to describe the general attributes of sensors. An application framework of the constructed indicators is developed to deeply mine the risks and operating situations for the wind speed sensor, including a sensitivity model for the weakness of NWEs, a resilience model for the recovery ability against NWEs, a performance evolution trend model for defective modes, and a clustering model for the general features of the sensor community in the wind farm. Experimental results on an actual wind farm dataset, including the operational data of 9 turbines for one year, show that the proposed method can characterize the differences in operating quality for wind speed sensors. Specifically, the proposed model can detect the sensitive sensors prone to defects by NWEs and identify the defective modes, including misalignment and destabilization. Wind speed sensors in the studied wind farms, sensitive to cold waves and strong convective NWEs, are likely subjected to performance degradation. The rate of sensitive sensors to disturbances in the studied wind farm reaches 33.3%. Most sensors can recover to smooth operation within 1 hour after NWEs. According to operation quality, the sensor community in the wind farm is classified into fluctuating, robust, and degraded sensors. The following conclusions can be drawn from the case analysis. (1) The proposed method can evaluate the operation behavior of wind speed sensors. The application system can further excavate the attributes of the sensor, such as perturbation sensitivity, resilience, and clustering characteristics, effectively identifying its weaknesses and shortcomings. (2) Typical defects of wind speed sensors and their trend features are sorted out. The proposed application model can characterize the operational situations of the sensors and describe the occurrence probability of defective patterns. (3) The proposed post-assessment model and application system can describe sensor operation behaviors, which guide the whole-life-cycle management, active inspection, and lean maintenance of wind farms.
Li Yang,Shen Xiaojun,Yang Weixin等. An Operation Quality Evaluation Method and Its Applications for Wind Speed Sensors of Wind Farms Based on Operation Data[J]. Transactions of China Electrotechnical Society, 2024, 39(13): 4188-4203.
[1] Li Yang, Shen Xiaojun.A novel wind speed sensing methodology for wind turbines based on digital twin technology[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-13. [2] 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. [3] Kusiak Andrew, Li Wenyan.The prediction and diagnosis of wind turbine faults[J]. Renewable Energy, 2011, 36(1): 16-23. [4] Li Yang, Shen Xiaojun.Anomaly detection and classification method for wind speed data of wind turbines using spatiotemporal dependency structure[J]. IEEE Transactions on Sustainable Energy, 2023, 14(4): 2417-2431. [5] Yin Shen, Xiao Bing, Ding Steven X, et al.A review on recent development of spacecraft attitude fault tolerant control system[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3311-3320. [6] 谭平, 蔡自兴, 余伶俐. 不同精度的冗余传感器故障诊断研究[J]. 控制与决策, 2011, 26(12): 1909-1912. Tan Ping, Cai Zixing, Yu Lingli.Research on fault diagnosis of different precision redundant sensors[J]. Control and Decision, 2011, 26(12): 1909-1912. [7] 李建平, 王晓凯. 基于模糊神经网络的无线传感器网络可靠性评估[J]. 计算机应用, 2016, 36(增刊2): 69-72. Li Jianping, Wang Xiaokai.WSN reliability evalu- ation based on fuzzy neural network[J]. Journal of Computer Applications, 2016, 36(S2): 69-72. [8] Xiang Shihu, Yang Jun.Reliability evaluation and reliability-based optimal design for wireless sensor networks[J]. IEEE Systems Journal, 2020, 14(2): 1752-1763. [9] 肖雄, 张勇军, 王京, 等. 无电压传感PWM整流器的虚拟磁链自适应滑模观测研究[J]. 电工技术学报, 2015, 30(12): 152-161. Xiao Xiong, Zhang Yongjun, Wang Jing, et al.PWM rectifiers based on adaptive sliding-mode observer with virtual flux orientation under non-line voltage sensors control[J]. Transactions of China Electro- technical Society, 2015, 30(12): 152-161. [10] 夏金辉, 郭源博, 张晓华. 单相脉宽调制整流器传感器故障诊断与容错控制[J]. 电工技术学报, 2017, 32(20): 160-170. Xia Jinhui, Guo Yuanbo, Zhang Xiaohua.Sensor fault diagnosis and fault tolerant control for single-phase PWM rectifier[J]. Transactions of China Electro- technical Society, 2017, 32(20): 160-170. [11] Yang Qingyu, Chen Yong.Monte Carlo methods for reliability evaluation of linear sensor systems[J]. IEEE Transactions on Reliability, 2011, 60(1): 305-314. [12] Alsarraj Ahmed, Rehman Atiq Ur, Belhaouari Samir Brahim, et al.Hydrogen sulfide (H2S) sensor: A concept of physical versus virtual sensing[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13. [13] Li Xiang, Zhang Wei, Xu Nanxi, et al.Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places[J]. IEEE Transactions on Industrial Electronics, 2020, 67(8): 6785-6794. [14] Zidi Salah, Moulahi Tarek, Alaya Bechir.Fault detection in wireless sensor networks through SVM classifier[J]. IEEE Sensors Journal, 2018, 18(1): 340-347. [15] 李阳, 沈小军. 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[J]. 电工技术学报, 2023, 38(7): 1793-1807. Li Yang, Shen Xiaojun.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. [16] 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. [17] Li Yang, Shen Xiaojun, Zhou Chongcheng.Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction[J]. Renewable Energy, 2023, 203: 841-853. [18] 严正, 李磊, 韩冬, 等. 基于改进超效率数据包络分析的低碳电力生产效率评估模型[J]. 电力系统自动化, 2014, 38(17): 170-176. Yan Zheng, Li Lei, Han Dong, et al.Evaluation model for low-carbon electricity production efficiency based on improved supper efficacy data envelopment analysis method[J]. Automation of Electric Power Systems, 2014, 38(17): 170-176. [19] 许寅, 和敬涵, 王颖, 等. 韧性背景下的配网故障恢复研究综述及展望[J]. 电工技术学报, 2019, 34(16): 3416-3429. Xu Yan, He Jinghan, Wang Ying, et al.A review on distribution system restoration for resilience enhance- ment[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3416-3429. [20] 王学伟, 刘建平, 袁瑞铭, 等. 复杂动态负荷电流幅度域典型游程波形模态与特征提取[J]. 电网技术, 2023, 47(6): 2497-2503. Wang Xuewei, Liu Jianping, Yuan Ruiming, et al.Extraction of typical run length waveform modes and characteristics of complex dynamic load current amplitude domain[J]. Power System Technology, 2023, 47(6): 2497-2503. [21] 李阳, 刘友波, 刘俊勇, 等. 基于形态距离的日负荷数据自适应稳健聚类算法[J]. 中国电机工程学报, 2019, 39(12): 3409-3420. Li Yang, Liu Youbo, Liu Junyong, et al.Self-adaptive and robust clustering algorithm for daily load profiles based on morphological distance[J]. Proceedings of the CSEE, 2019, 39(12): 3409-3420.
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