基于散点图-AlexNet网络的光伏红外热图像识别方法

孙海蓉; 周映杰

doi:10.19912/j.0254-0096.tynxb.2021-0811

PDF(2321 KB)

太阳能学报 ›› 2023, Vol. 44 ›› Issue (1) : 55-61. DOI: 10.19912/j.0254-0096.tynxb.2021-0811

基于散点图-AlexNet网络的光伏红外热图像识别方法

孙海蓉^1,2, 周映杰^1,2

作者信息 +

PHOTOVOLTAIC INFRARED THERMAL IMAGE RECOGNITION METHOD BASED ON SCATTER PLOT-AlexNet NETWORK

Sun Hairong^1,2, Zhou Yingjie^1,2

Author information +

文章历史 +

摘要

针对神经网络在光伏发电中对光伏红外热图像识别准确率和训练效率低的问题,提出基于散点图-AlexNet网络的识别模型。首先将光伏红外热像图对应的HSI空间数据信息直接投射到平面坐标系中,形成由若干散点构成的可视化RGB三色图像,并使用基于AlexNet网络的快速AlexNet网络进行训练。为了证明新方法的优良性能,选取识别准确率和单次训练时间对模型进行评价。实验结果表明,该方法识别率高,能准确识别光伏红外热图像中的图片种类,且训练效率也显著提升。

Abstract

Aiming at the low recognition accuracy and training efficiency of neural network for photovoltaic infrared thermal image in photovoltaic generation, a recognition model based on scatter plot-AlexNet network is proposed. First, the HSI spatial data information corresponding to the photovoltaic infrared thermal image is directly projected into the plane coordinate system to form a visual RGB three-color image composed of several scattered points, and the rapid AlexNet network based on the AlexNet network is used for training. In order to prove the excellent performance of the new method, the recognition accuracy and single training time are selected to evaluate the model. Experimental results show that this method has high recognition rate, can accurately identify the types of images in photovoltaic infrared thermal images, and the training efficiency is significantly improved.

导出引用

孙海蓉, 周映杰. 基于散点图-AlexNet网络的光伏红外热图像识别方法[J]. 太阳能学报. 2023, 44(1): 55-61 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0811

Sun Hairong, Zhou Yingjie. PHOTOVOLTAIC INFRARED THERMAL IMAGE RECOGNITION METHOD BASED ON SCATTER PLOT-AlexNet NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(1): 55-61 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0811

中图分类号： TP183 TM914.4

参考文献

[1] XU J P, WANG F J, LV C W, et al.Carbon emission reduction and reliable power supply equilibrium based daily scheduling towards hydro-thermal-wind generation system: a perspective from China[J]. Energy conversion and management, 2018, 164: 1-14.
[2] SQUALLI J.Renewable energy, coal as a baseload power source, and greenhouse gas emissions: evidence from U. S. state-level data[J]. Energy, 2017, 127: 479-488.
[3] ESPÉCIE M D A, DE CARVALHO P N, BACILE PINHEIRO M F, et al. Ecosystem services and renewable power generation: a preliminary literature review[J]. Renewable energy, 2019, 140: 39-51.
[4] 范思远, 姚显双, 曹生现, 等. 基于延迟回声状态网的光伏组件温度预测方法[J]. 自动化学报, 2020, 46(12): 2701-2710.
FAN S Y, YAO X S, CAO S X, et al.Temperature prediction of photovoltaic panels based on delayed echo state network[J]. Acta automatica sinica, 2020, 46(12):2701-2710.
[5] PEACOK T M, SARIOL H C, YPERMAN J, et al.Infrared thermography: a new approach for the characterization and management of activated carbons applied in water treatment[J]. Chemical engineering science, 2021, 246: 116881.
[6] 蒋琳, 苏建徽, 施永, 等. 基于红外热图像处理的光伏阵列热斑检测方法[J]. 太阳能学报, 2020, 41(8): 180-184.
JIANG L, SU J H, SHI Y, et al.Hot spots detection of operating PV arrays through IR thermal image[J]. Acta energiae solaris sinica, 2020, 41(8): 180-184.
[7] 郝惠敏, 梁永国, 武海彬, 等. 对称点模式-深度卷积神经网络的红外光谱识别方法[J]. 光谱学与光谱分析, 2021, 41(3): 782-788.
HAO H M, LIANG Y G, WU H B, et al.Infrared spectrum recognition method based on symmetrized dot pattern coupled with deep convolutional neural network[J]. Spectroscopy and spectral analysis, 2021, 41(3):782-788.
[8] CEYLAN İ, ERKAYMAZ O, GEDIKG E, et al.The prediction of photovoltaic module temperature with artificial neural networks[J]. Case studies in thermal engineering, 2014, 3: 11-20.
[9] SUN Y J, WANG F, ZHEN Z, et al.Research on short-term module temperature prediction model based on BP neural network for photovoltaic power forecasting[C]//Proceedings of the 2015 IEEE Power and Energy Society General Meeting, Colorado, USA, 2015: 1-5.
[10] SUN Y J, WANG F, MI Z Q, et al.Short-term prediction model of module temperature for photovoltaic power forecasting based on support vector machine[C]//Proceedings of the 2015 International Conference on Renewable Power Generation, Beijing, China, IET, 2015.
[11] 郭梦浩, 徐红伟. 基于Faster RCNN的红外热图像热斑缺陷检测研究[J]. 计算机系统应用, 2019, 28(11):265-270.
GUO M H, XU H W.Hot spot defect detection based on infrared thermal image and faster RCNN[J]. Computer systems & applications, 2019, 28(11): 265-270.
[12] 贾帅康, 白英君, 孙海蓉, 等. 基于DenseNet的红外图像热斑状态分类研究[J]. 山东电力技术, 2021, 48(3): 60-64.
JIA S K, BAI Y J, SUN H R, et al.Research on classification of hot spot state in infrared image based on DenseNet[J]. Shandong electric power, 2021, 48(3): 60-64.
[13] 孙海蓉, 潘子杰, 晏勇. 基于深度卷积自编码网络的小样本光伏热斑识别与定位[J]. 华北电力大学学报(自然科学版), 2021, 48(4): 91-98.
SUN H R, PAN Z J, YAN Y.Identification and location of photovoltaic hot spots with small samples based on deep convolutional autoencoder network[J]. Journal of North China Electric Power University(natural science edition), 2021, 48(4): 91-98.
[14] SIKAKOLLU P, DASH R.Ensemble of multiple CNN classifiers for HSI classification with superpixel smoothing[J]. Computers & geosciences, 2021, 154: 104806.
[15] 曾晟, 梁乃兴, 杨遂中, 等. HSI空间在低照度沥青混合料图像处理中的应用[J]. 重庆交通大学学报(自然科学版), 2020, 39(11): 86-91.
ZENG S, LIANG N X, YANG S Z, et al.Application of HSI color space in image processing of asphalt mixture under low illumination conditions[J]. Journal of Chongqing Jiaotong University(natural science edition), 2020, 39(11): 86-91.
[16] 胡英奎, 陈仲林, 张青文, 等. 考虑驾驶员视觉适应的公路隧道入口段亮度确定方法[J]. 土木建筑与环境工程, 2016, 38(4): 20-26.
HU Y K, CHEN Z L, ZHANG Q W, et al.Tunnel threshold zone luminance determined method based on drivers’ visual adaptation[J]. Journal of civil, architectural & environmental engineering, 2016, 38(4): 20-26.
[17] 付青, 罗文浪, 吕敬祥. 基于AlexNet和支持向量机相结合的卫星遥感影像土地利用变化检测[J]. 激光与光电子学进展, 2020, 57(17): 282-290.
FU Q,LUO W L, LYU J X.Land utilization change detection of satellite remote sensing image based on AlexNet and support vector machine[J]. Laser & optoelectronics progress, 2020, 57(17): 282-290.
[18] KRIZHEVSKY A, SUTSKEVER I, GEOFFREY E H.ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[19] LEDLEY R S, BUAS M, GOLAB T J.Fundamentals of true-color image processing[C]//Proceedings of the 10th International Conference on Pattern Recognition, Atlantic, GA, USA, 1990.
[20] LUO H L, LIN D F, YU C C, et al.Application of different HSI color models to detect fire-damaged mortar[J]. International journal of transportation science and technology, 2014, 2(4): 303-316.
[21] NNOLIM U A.Design and implementation of novel, fast, pipelined HSI2RGB and log-hybrid RGB2HSI colour converter architectures for image enhancement[J]. Microprocessors and microsystems, 2015, 39(4/5): 223-236.
[22] ZHAO Z Y, XU S X, KANG B H, et al.Investigation and improvement of multi-layer perceptron neural networks for credit scoring[J]. Expert systems with applications, 2015, 42(7): 3508-3516.
[23] HE M, ZHAO X D, LU Y, et al.An improved AlexNet model for automated skeletal maturity assessment using hand X-ray images[J]. Future generation computer systems, 2021, 121: 106-113.
[24] LI J J, LIU D.Information bottleneck theory on convolutional neural networks[J]. Neural processing letters, 2021, 53: 1385-1400.