Operation reliability analysis of low-voltage collection reading system based on LoRa technology

Yiqun Wang; Zhi Zhang; Yuejia Wu; Xin Ou; Tianqiang Dong; Hang Sun; Yuan Xu

doi:10.1117/12.2670479

1 June 2023 Operation reliability analysis of low-voltage collection reading system based on LoRa technology

Yiqun Wang, Zhi Zhang, Yuejia Wu, Xin Ou, Tianqiang Dong, Hang Sun, Yuan Xu

Proceedings Volume 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022); 1262505 (2023) https://doi.org/10.1117/12.2670479
Event: International Conference on Mathematics, Modeling and Computer Science (MMCS2022),, 2022, Wuhan, China

Abstract

The integration of low-voltage data collection system is relatively high, and there are some problems such as long distance between nodes. Therefore, while testing the operation reliability of the system, it is often difficult to analyze and the accuracy is low because of power consumption. Based on that, a reliability analysis method for low-voltage centralized reading system based on LoRa technology is proposed in this paper. Under LoRa framework protocol, the evaluation index of system reliability is defined, and the running state of the system is determined by calculating the state transition rate, the stationary state probability and the instantaneous state of the components. With this basis, the load carrying capacity of each node and the risk of voltage collapse are calculated respectively. Through setting up control group and experimental group to carry out detailed empirical analyses, the reliability analysis results of the system are obtained. Through comparison, it is found that the average analysis accuracy of the system operation reliability analysis method designed is 25% higher than that of the traditional method.

Citation Download Citation

Yiqun Wang, Zhi Zhang, Yuejia Wu, Xin Ou, Tianqiang Dong, Hang Sun, and Yuan Xu "Operation reliability analysis of low-voltage collection reading system based on LoRa technology", Proc. SPIE 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022), 1262505 (1 June 2023); https://doi.org/10.1117/12.2670479

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