SELF-CONFIRMATION OF ONBOARD ANEMOMETER STATUS OF WIND TURBINES IN WIND FARMS

Zhou Ling; Zhao Qiancheng; Shi Zhaoyao; Wang Xian; Yang Xuebing

doi:10.19912/j.0254-0096.tynxb.2021-0709

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (11) : 172-178. DOI: 10.19912/j.0254-0096.tynxb.2021-0709

SELF-CONFIRMATION OF ONBOARD ANEMOMETER STATUS OF WIND TURBINES IN WIND FARMS

Zhou Ling^1,2, Zhao Qiancheng¹, Shi Zhaoyao^1,3, Wang Xian¹, Yang Xuebing^1,4

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Abstract

In this study, a method of sensor status self-confirmation is proposed, taking the wind turbine airborne anemometer as an example. A group of wind turbines is selected by a dynamic time warping algorithm, following the correlation of wind speed of multiple wind turbines. The prediction model of the wind turbine group anemometer based on an auto-association neural network is constructed. The model is trained by the sparrow search optimization algorithm with normal historical data, and the state of the anemometer is determined according to the relationship between the actual value and the predicted value. The simulation experiment proves that the method can identify the abnormal state of anemometer simulation. Finally, the actual wind speed of the wind farm is detected. The results show that this method can reliably identify the state of the anemometer and acquire the self-confirmation of the state of the anemometer of the wind turbine.

Key words

wind turbine / anemometer / auto-associative neural network / sparrow search algorithm / state self -confirmation

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Zhou Ling, Zhao Qiancheng, Shi Zhaoyao, Wang Xian, Yang Xuebing. SELF-CONFIRMATION OF ONBOARD ANEMOMETER STATUS OF WIND TURBINES IN WIND FARMS[J]. Acta Energiae Solaris Sinica. 2022, 43(11): 172-178 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0709

References

[1] KAYIKI M, MILANOVIC J V.Reactive power control strategies for DFIG based plants[J]. IEEE transactions on energy conversion, 2007, 2(2): 389-396.
[2] 李辉, 赵猛, 赵斌, 等. 双馈风电机组关键传感器的故障诊断方法[J]. 中国电机工程学报, 2011, 31(6): 73-78.
LI H, ZHAO M, ZHAO B, et al.Fault diagnosis method for key sensors of doubly-fed wind turbines[J]. Proceedings of the CSEE, 2011, 31(6): 73-78.
[3] 金晓航, 孙毅, 单继宏, 等. 风力发电机组故障诊断与预测技术研究综述[J]. 仪器仪表学报, 2017, 38(5): 1041-1053.
JIN X H, SUN Y, SHAN J, et al.Fault diagnosis and prognosis for wind turbines: an overview[J]. Chinese journal of scientific instrument, 2017, 38(5): 1041-1053.
[4] 房亮, 刘平, 张博. 双馈风电机控制系统传感器的故障仿真[J]. 仪表技术, 2014(2): 52-54.
FANG L, LIU P, ZHANG B.Fault simulation of sensors in doubly-fed wind turbine control system[J]. Instrumentation technology, 2014(2): 52-54.
[5] 向玲, 邓泽奇, 赵玥. 基于SCADA数据的风电机组异常识别方法[J]. 太阳能学报, 2020, 41(11): 278-284.
XIANG L, DENG Z Q, ZHAO Y.Anomaly recognition method for wind turbines based on SCADA data[J]. Acta energiae solaris sinica, 2020, 41(11): 278-284.
[6] 张帆, 刘德顺, 戴巨川, 等. 一种基于SCADA参数关系等风电继续运行状态识别方法[J]. 机械工程学报, 2019, 55(4): 1-9.
ZHANG F, LIU D S, DAI J C, el al. An operating condition recognition method of wind turbine based on SCADA parameter relations[J]. Journal of mechanical engineering, 2019, 55(4): 1-9.
[7] PHONG B D, WIESLAW J S, TOMASZ B, el al. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data[J]. Renewable energy, 2018, 116: 107-122.
[8] PETER F O, JAKOB S, MICHEL K.Fault-tolerant control of wind turbines: a Benchmark model[J]. IEEE transactions on control systems technology, 2013, 21(4): 1168-1182.
[9] 李东亮, 文传博. 基于改进SMO的风电机组降阶系统速度传感器故障检测[J]. 电力科学与工程, 2017, 33(2): 60-65.
LI D L, WEN C B.Speed sensor fault detection of wind turbine reduction system based on improved SMO[J]. Electric power science and engineering, 2017, 33(2): 60-65.
[10] KRAMER M.Autoassociative neural networks neutral network applications in chemical engineering[J]. Computers & chemical engineering, 1992, 16(4): 313-328.
[11] MOUSAVI H, SHAHBAZIAN M, JAZAYERI R H, el al. Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks[J]. Journal of Central South University, 2014, 21: 2273-2281.
[12] VANINI Z N S,MESKIN N,KHORASANI K. Multiple-model sensor and components fault diagnosis in gas turbine engines using autoassociative neural networks[J]. Journal of engineering for gas turbines and power, 2014, 136(9): 603-609.
[13] MARIAM E, NADER M, MOHAMMED A N.Sensor fault diagnosis of multi-zone HVAC systems using auto-associative neural network[C]//2019 IEEE Conference on Control Technology and Applications (CCTA), Hong Kong, China, 2019.
[14] 孟祥泽, 胡啸峰, 沈兵. 社区老年人空间行为轨迹异常分析方法[J]. 科学技术与工程, 2021, 21(9): 3676-3681.
MENG X Z, HU X F, SHEN B.Study on abnormal analysis method of old peoples spatial behavior trajectory in community[J]. Science technology and engineering, 2021, 21(9): 3676-3681.
[15] 刘鲭洁, 陈桂明, 刘小方. BP神经网络权重和阈值初始化方法研究[J]. 西南师范大学学报(自然科学版), 2010, 35(6): 12-15.
LIU Q J, CHEN G M, LIU X F.Reserarch on initializaation algorithms of weightsand biases of BP neural network[J]. Journal of Southwest China Normal University(natural science edition), 2010, 35(6): 12-15.
[16] JIAN K X,BO S.A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems science & control engineering, 2020, 8(1): 22-34.