针对光伏电站结构安全中螺栓松动的检测与定位问题,提出一种基于链式电阻传感网络的光伏电站紧固状态监测系统。首先,基于螺栓与螺母相对转动导致螺栓松动原理,针对光伏组件不同螺栓结构,分别设计板面传感器与支架传感器,将螺栓的转动转换为电阻信号;其次,根据光伏组件中板面螺栓和支架螺栓的分布特点,分别设计板面正反向检测电路和支架检测电路,通过检测电路总电阻的变化,计算螺栓松动位置的定位及松动数量;根据二极管I-V特性,提出一种电压电流差值比值法,减少了二极管对总阻值的影响,提高了板面松动螺栓数量检测精度。实验结果表明,该系统能准确检测光伏电站中螺栓的松动位置及数量,检测容量随着检测电压和传感器电阻的增加而增加,实现了螺栓松动预警远程监测,为分布式光伏电站的螺栓状态监测提供了一种高效、可靠的解决方案。
Abstract
To address the issue of bolt loosening detection and localization in the structural safety of photovoltaic （PV） power stations, this paper proposes a PV fastening state monitoring system based on a chained resistive sensor network. Firstly, based on the principle that bolt loosening is caused by the relative rotation between the bolt and nut, surface sensors and bracket sensors are designed for different bolt structures in PV modules, converting bolt rotation into resistance signals. Secondly, considering the distribution characteristics of surface bolts and bracket bolts, forward and reverse detection circuits for the surface and detection circuits for the bracket are respectively designed. By monitoring the changes in the total resistance of the detection circuit, the system can determine the position and quantity of loosened bolts. Based on the I-V characteristics of diodes, a voltage-current difference ratio method is proposed to reduce the impact of diode internal resistance on the total resistance, thereby improving the accuracy of surface detecting the number of loosened surface bolts. Experimental results show that the system can accurately detect the location and number of loosened bolts in PV power stations. The detection capacity increases with higher detection voltage and sensor resistance. This system enables remote monitoring and early warning of bolt loosening, providing an efficient and reliable solution for bolt status monitoring in distributed PV power stations.
关键词
光伏组件 /
螺栓 /
二极管 /
传感器 /
检测电路 /
电压电流差值比值法
Key words
PV modules /
bolts /
diodes /
sensor /
detection circuit /
voltage-current difference ratio method
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 曹岚, 周成功, 袁斌霞, 等. 基于梯度提升回归树-自训练贝叶斯优化支持向量机的光伏阵列故障诊断方法[J]. 太阳能学报, 2025, 46(6): 289-297.
Cao L, Zhou C G, Yuan B X, et al.Fault diagnosis methods of photovoltaic arrays based on gradient boosting regression tree-self-training Bayesian optimized support vector machine[J]. Acta Energiae Solaris Sinica, 2025, 46(6): 289-297.
[2] 王捷, 林余杰, 吴成坚, 等. 碳中和背景下太阳能光伏产业现状及发展[J]. 储能科学与技术, 2022, 11(2): 731-732.
Wang J, Lin Y J, Wu C J, et al.Present situation and development of solar photovoltaic industry under the background of carbon neutrality[J]. Energy Storage Science and Technology, 2022, 11(2): 731-732.
[3] 胡昕, 付国辉, 王利中, 等. 光伏发电系统中建筑结构安全性评价的现状及必要性[J]. 山西建筑, 2017, 43(36): 225-227.
Hu X, Fu G H, Wang L Z, et al.On current status of safety evaluation in architectural structures in photovoltaic power generation system and its necessity[J]. Shanxi Architecture, 2017, 43(36): 225-227.
[4] 劳武略, 徐威, 张清华, 等. 基于计算机视觉的钢桥螺栓松动检测方法[J]. 铁道学报, 2024, 46(1): 91-102.
Lao W L, Xu W, Zhang Q H, et al.Computer vision-based detection method for steel bridge bolt-looseness[J]. Journal of the China Railway Society, 2024, 46(1): 91-102.
[5] Deng L, Sa Y, Li X F, et al.A vision-based bolt looseness detection method for a multi-bolt connection[J]. Applied Sciences, 2024, 14(11): 4385.
[6] Wang T, Song G B, Wang Z G, et al.Proof-of-concept study of monitoring bolt connection status using a piezoelectric based active sensing method[J]. Smart Materials and Structures, 2013, 22(8): 087001.
[7] 宋琛琛, 谢丽宇, 薛松涛. 基于压电阻抗技术的螺栓松动监测试验研究[J]. 公路交通科技, 2016, 33(4): 113-119.
Song C C, Xie L Y, Xue S T.Experimental study of bolt loosening detection based on piezoelectricimpedance technology[J]. Journal of Highway and Transportation Research and Development, 2016, 33(4): 113-119.
[8] 王怡, 王宁, 卢萍, 等. 基于声发射原理的螺栓连接状态辨识方法研究[J]. 声学技术, 2010, 29(5): 453-456.
Wang Y, Wang N, Lu P, et al.The study of identifying the state of bolted joint structure based on acoustic emission principle[J]. Technical Acoustics, 2010, 29(5): 453-456.
[9] Onishi M,Y Mizutani, Mayuzumi M. Plastic region bolt tightening controlled by acoustic emission monitoring[J]. Journal of acoustic emission,2005,23(12): 285-291.
[10] Zhu Y G, Wang G, Lin B, et al.Effects of bolt anchoring on shear mechanical performance and acoustic emission characteristics in two parallel coplanar rock-like materials[J]. International journal of geomechanics,2024,24(8): 04024158.
[11] Zhang Z, Xiao Y, Su Z Q, et al.Continuous monitoring of tightening condition of single-lap bolted composite joints using intrinsic mode functions of acoustic emission signals: a proof-of-concept study[J]. Structural Health Monitoring, 2019, 18(4): 1219-1234.
[12] Huynh T C, Park J H, Jung H J, et al.Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing[J]. Automation in Construction, 2019, 105: 102844.
[13] Wang C Y, Wang N, Ho S C, et al.Design of a new vision-based method for the bolts looseness detection in flange connections[J]. IEEE Transactions on Industrial Electronics, 2020, 67(2): 1366-1375.
[14] Yan X D, Song X G.An Image Recognition Algorithm of Bolt Loss in Underground Pipelines Based on Local Binary Pattern Operator[J]. Traitement du Signal: signal image parole, 2020, 37(4): 679-685.
[15] Zhang R, Guan C D, Sui X D, et al.Computer Vision-Based Quantitative Detection of Bolt Loosening Using Two-Stage Perspective Distortion Correction Method[J]. Journal of Nondestructive Evaluation, 2025, 44(3):71.
[16] Huo L S, Chen D D, Kong Q Z, et al.Smart washer: a piezoceramic-based transducer to monitor looseness of bolted connection[J]. Smart Materials and Structures, 2017, 26(2): 025033.
[17] Wang T, Wang H, Yang D, et al.Early bolt looseness monitoring using the leading waves energy in piezoelectric active sensing[J]. Smart Materials & Structures, 2024, 33(3): 035033.
[18] Park J K, Park S.Intelligent bolt-jointed system integrating piezoelectric sensors with shape memory alloys[J]. Smart Structures and Systems,2016,17(1):135-147.
[19] 陈权, 牛华伟, 李红星, 等. 基于风洞试验与规范规定值的光伏支架风荷载取值研究[J]. 太阳能学报, 2024, 45(9): 259-267.
Chen Q, Niu H W, Li H X, et al.Research on wind load value of photovoltaic bracket based on wind tunnel test and standard values[J]. Acta Energiae Solaris Sinica, 2024, 45(9): 259-267.
基金
国家自然科学基金（62371261）; 南通市社会民生科技计划项目（MSZ2024167）
PDF(2904 KB)
