Number of images - 4
Tables and charts - 3
Solar Photovoltaic Power Generation Wireless Monitoring System Based on IOT Technology. L&E 26 (4) 2018

Light & Engineering 26 (4)

Volume 26
Date of publication 12/20/2018
Pages 130–136


Solar Photovoltaic Power Generation Wireless Monitoring System Based on IOT Technology. L&E 26 (4) 2018
Articles authors:
Erhua SUN

Erhua SUN, Master of Business Administration, Associate Professor. Graduated from the Chongqing communication college in 2001. Worked in Chongqing real estate college. Her research interests are data analysis and data mining

In order to further improve the real­time detection of power generation, a wireless monitoring model of solar photovoltaic power generation based on Internet of things (IoT) technology is proposed. Firstly, the application of remote monitoring in power generation technology is introduced, and the monitoring model of solar power equipment is constructed by wireless network, and the corresponding feedback mechanism is established by means of the (IoT) algorithm. Finally, the data processing ability and analysis effect of the wireless monitoring model are tested and studied. The test results show that the monitoring model can record and optimize the solar power generation data in real time, which greatly reduces the failure rate in power generation. It is proved that the monitoring model used in this paper has good feedback effect.
1. Zabidi A., Yassin I M., Hassan H A. Detection of asphyxia in infants using deep learning convolutional neural network (CNN) trained on Mel frequency cepstrum coefficient (MFCC) features extracted from cry sounds. Journal of Fundamental and Applied Sciences, 2017, V9, #3S, pp.768–778.
2. Sirinukunwattana K., Raza S., Tsang Y W. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Transactions on Medical Imaging, 2016, V35, #5, pp.1196–1206.
3. Chang, C., Qiang, Z., Zhongjian, L. Design of wireless power supply optimized structure for capsule endoscopes. Journal of Power Technologies, 2016, V96, #2, pp.101–109.
4. Gan M., Wang C., Zhu C. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems & Signal Processing, 2016, V72, #2, pp.92–104.
5. Weinan E., Han J., Jentzen A. Deep Learning­Based Numerical Methods for High­Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations. Communications in Mathematics & Statistics, 2017, V5, #4, pp.349–380.
6. Abramoff M D., Lou Y., Erginay A. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Ophthalmology & Visual Science, 2016, V57, #13, p.5200.
7. Cocos A., Fiks A G., Masino A J. Deep learning for pharmacovigilance: recurrent neural network architectures for labelling adverse drug reactions in Twitter posts. Journal of the American Medical Informatics Association, 2017, V24, #4, pp.813–821.
8. Wang C., Cunefare D., Fang L. Automatic segmentation of nine retinal layer boundaries in OCT images of non­exudative AMD patients using deep learning and graph search. Biomedical Optics Express, 2017, V8, #5, pp.2732–2744.
9. Weiping Zhang., Akbar Maleki., Marc A. Rosen., Jingqing Liu. Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage, Energy, 2018, V163, pp. 191–207.
10. Kumar, M., Mao, YH., Wang, YH., Qiu, TR., Yang, C., Zhang, WP. Fuzzy theoretic approach to signals and systems: Static systems, Information Sciences, 2017, V418, pp.668–702.
Recommended articles