|本期目录/Table of Contents|

[1]王承凯,边 辉,张家友.基于多风电机组风速变量关联复杂网络的风速仪故障检测方法[J].工业仪表与自动化装置,2025,(01):66-73.[doi:10.19950/j.cnki.CN61-1121/TH.2025.01.012]
　WANG Chengkai,BIAN Hui,ZHANG Jiayou.Fault detection method of anemometer based on complex network of wind speedvariable correlation in multiple wind turbines[J].Industrial Instrumentation & Automation,2025,(01):66-73.[doi:10.19950/j.cnki.CN61-1121/TH.2025.01.012]
点击复制

基于多风电机组风速变量关联复杂网络的风速仪故障检测方法(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2025年01期
页码:
66-73
栏目:
出版日期:
2025-02-15

文章信息/Info

Title:
Fault detection method of anemometer based on complex network of wind speedvariable correlation in multiple wind turbines
文章编号:
1000-0682（2025）01-0066-08
作者:
王承凯; 边 辉; 张家友
（1.国华能源投资有限公司，北京 100007；2.国华（沽源）风电有限公司，河北 张家口 076550；3.中车株洲电力机车研究所有限公司，湖南 株洲 412001）
Author(s):
WANG Chengkai;  BIAN Hui;  ZHANG Jiayou
（1. Guohua Energy Investment Co., Ltd., Beijing 100007, China;2. Guohua (Guyuan) Wind Power Co., Ltd., Hebei Zhangjiakou 076550, China;3. CRRC Zhuzhou Institute Co.,Ltd., Hunan Zhuzhou, 412001,China）
关键词:
风力发电; 风速仪故障; 复杂网络; 无标度网络; 网络结构熵
Keywords:
wind power generation;  anemometer malfunction;  complex networks;  scale-free network;  network structure entropy
分类号:
TH7
DOI:
10.19950/j.cnki.CN61-1121/TH.2025.01.012
文献标志码:
A
摘要:
该文基于多风电机组风速变量的关联复杂网络，提出了一种风速仪故障检测方法。通过符号化表征机组间风速关联关系，构建复杂网络模型，并统计分析网络演化。研究发现，多机组风速关联网络的度分布呈幂律特征，属于复杂网络中的无标度网络类型，揭示出网络结构的高度不均匀性，其中核心由少数关键节点构成。在正常情况下，网络结构稳定；一旦某机组风速仪发生故障，网络结构将发生显著变化，关键节点改变，同时网络结构熵降低。通过分析多个复杂网络的机组组合的交集，能精确诊断出发生风速仪故障的机组。该方法兼具时效性和定量诊断的特性，可以实现风速仪故障及时告警及辅助机组作出响应，以确保机组安全运行，进而有助于提升发电量。
Abstract:
This paper proposes a fault detection method for anemometers, which is based on the complex network of wind speed variables among multiple wind turbine generators. By symbolizing the wind speed correlation between turbines, a complex network model was constructed, and the network evolution was statistically analyzed. The research reveals that the degree distribution of the multi-turbine wind speed correlation network exhibits a power-law characteristic, falling into the category of scale-free networks in complex networks, indicating a high degree of inhomogeneity in the network structure, with a core comprising a few key nodes. Under normal conditions, the network structure remains stable; The anemometer failed on a certain wind turbine, then the network structure would change significant , with key nodes altered and the network structural entropy decreased. By analyzing the intersection of turbine combinations in multiple complex networks, this method can accurately diagnose the turbine with a malfunctioning anemometer. This method possesses both timeliness and quantitative diagnostic features, and its application in wind farm intelligent control enables timely warnings of anemometer failures and assisting turbines in making responses, contributing to ensuring the safe operation of turbines and enhancing power generation.

参考文献/References:

[1]ZHOU Ling, ZHAO Qiancheng, WANG Xian, et al. Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN[J]. Energies, 2021, 14(21):6905.

[2]ZHANG Jiusi,?LI Kuan, LUO Hao,et al. A Data-Driven Fault Diagnosis Approach for Anemometers in Wind Farm[C]. // Singapore: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society,2020: 405–410.
[3]J Beltrán, Guerrero J J, Melero J J, et al.Detection of nacelle anemometer faults in a wind farm minimizing the uncertainty[J]. Wind Energy, 2013, 16(6): 939-952.
[4]魏敏,陈克锐,刘军,等.基于邻近差异分析的风速仪故障诊断[J].云南水力发电,2020,36(9): 27-30.
[5]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.
[6]周凌,赵前程,石照耀,等.风电场风电机组机载风速仪状态自确认[J]. 太阳能学报, 2022, 43(11): 172-178.?/div>
[7]胡钢,牛琼,许丽鹏,等.基于网络超链接信息熵的节点重要性序结构演化建模分析[J].电子学报,2022,50(11): 2638-2644.
[8]高建民,谢军太,高智勇,等.复杂工业过程的关联波动网络建模与分析[J].控制理论与应用,2015,34(8):7-12.
[9]丁小欧,于晟健,王沐贤,等.基于相关性分析的工业时序数据异常检测[J]. 软件学报, 2021(3): 726-747.
[10]陈增强.网络科学中的度量分析与应用[M].北京:化学工业出版社会, 2019: 69-79, 88-97.
[11]李锴,何永锋.基于度值标准差的复杂网络可靠性[J]. 计算机工程与设计, 2018, 39(2): 306-310.
[12]汪小帆.无标度网络研究纷争: 回顾与评述[J]. 电子科技大学学报, 2020, 49(4): 499-510.
[13]姚宗露.无标度网络中幂律分布的贝叶斯推断[D].长春:吉林大学, 2020.
[14]尹荣荣,尹学良,崔梦頔,等.基于重要贡献的无标度网络节点评估方法[J].软件学报,2019,30(6):1875-1885.
[15]冯龙飞,高建民,高智勇,等.基于DCCA-NSEn的系统耦合网络建模与评估[J].振动、测试与诊断,2019,39(5): 1046-1052.
[16]YANG Yunyun, FENG Biao, ZHANG Liao, et al. Robustness measurement of scale-free networks based on motif entropy[J].中国物理B（英文版）, 2022, 31(8): 211-217.
[17]MEMG Cai, YING Cui, HE Stanley. Analysis and Evaluation of the Entropy Indices of a Static Network Structure. Scientific Reports. 2017, 7(1): 9340
[18]ZHANG Qi, LI Meizhu, DENG Yong. A Betweenness Structural Entropy of Complex Networks[J]. Chaos, Solitons and Fractals: Applications in Science and Engineering: An Interdisciplinary Journal of Nonlinear Science,2022,116(8): 112264.
[19]周丽娜,常笑,胡枫.利用邻接结构熵确定超网络关键节点[J].计算机工程与应用, 2022, 58(8), 76-82.
[20]张诗琪,李琰,徐天奇.基于复杂网络理论的电力信息物理系统耦合建模方式研究与脆弱性分析[J].云南民族大学学报(自然科学版), 2022,31(6): 762-769.
[21]文坤,万宇宾,胡凯凯,等.基于风机风向相关系数的风向仪共享控制方法研究[J]. 控制与信息技术, 2022(1): 47-52.

相似文献/References:

[1]郭涛,马林东,葛智平,等.关于高耗能大工业用户提高大规模风电消纳能力的研究[J].工业仪表与自动化装置,2013,(05):17.
[2]陈 伟,王小龙,张浩琛.电压跌落时双馈电机电磁暂态过程仿真与分析[J].工业仪表与自动化装置,2014,(05):10.
　CHEN Wei,WANG Xiaolong,ZHANG Haochen.Simulation and analysis of doubly-fed induction generator transient response during voltage dip[J].Industrial Instrumentation & Automation,2014,(01):10.
[3]党存禄,杜小波.粒子群优化变论域模糊PID控制在风电机组变桨距中的应用[J].工业仪表与自动化装置,2017,(05):97.
　DANG Cunlu,DU Xiaobo.Application of variable universe fuzzy PID control optimized by particle swarm in variable pitch of wind turbine[J].Industrial Instrumentation & Automation,2017,(01):97.

备注/Memo

备注/Memo:
收稿日期：2024-06-28基金项目：湖南省自然科学基金项目/科教联合项目（2022JJ60074）第一作者：王承凯（1976—），男，高级工程师，研究方向为风电项目开发和生产运营管理。E-mail：987654@163.com
更新日期/Last Update: 1900-01-01