scholarly journals Design and Application of Transmission Line Intelligent Monitoring System

2020 ◽  
Vol 185 ◽  
pp. 01063
Author(s):  
Zengqiang Xing ◽  
WenpengCui Cui ◽  
Rui Liu ◽  
Zhe Zheng
Keyword(s):  
Artificial Intelligence ◽  
Transmission Line ◽  
Monitoring System ◽  
Network Model ◽  
Recognition Accuracy ◽  
Design Method ◽  
Target Recognition ◽  
Image Resolution ◽  
Intelligent Monitoring ◽  
Intelligent Recognition

This paper presents a design method of intelligent monitoring system for transmission lines based on artificial intelligence technology. In this design method, a low-power artificial intelligence chip - LieYing A101 is used to design an intelligent recognition module to realize real-time target recognition on a terminal device. In order to solve the problem that the original image and the input image resolution of the intelligent recognition module do not match, this paper uses a sliding window and convolutional neural network design method, which solves the image resolution mismatch problem and improves the recognition accuracy. Finally, for the problem of excessive network model size, feature channel weight pruning and 8-bit quantization methods are used to compress the network model to less than 10M, and the recognition accuracy is not sharply reduced. After the test set test and actual scene use, the external force destruction target recognition accuracy of the transmission line channel is high; this meets the application needs of customers.

The Design of Transmission Line Monitoring System Based on Wireless Sensor Network

2012 ◽  
Vol 490-495 ◽  
pp. 1465-1469
Author(s):  
Xin Hou ◽  
Yu Jing Kong ◽  
Er Tian Hua
Keyword(s):  
Wireless Sensor Network ◽  
Transmission Line ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Modular Design ◽  
Design Method ◽  
Rapid Development ◽  
Wireless Sensor ◽  
The Real

With the rapid development of the power system, the real-time monitoring of the transmission line state becomes more and more important. Due to the feature of wireless sensor network, a new scheme of monitoring system for transmission line state is proposed. The modular design method is used to choose appropriate modules for setting up the monitoring system, which realizes the real-time temperature, humidity, light, atmospheric pressure on the transmission line. The reliability and practicability is proved by the experiments.

scholarly journals Intelligent Recognition of Transmission Line Inspection Image Based on Deep Learning

2021 ◽  
Vol 1757 (1) ◽  
pp. 012056
Author(s):  
Yue Qi ◽  
Shile Mu ◽  
Jun Wang ◽  
Liangliang Wang
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Intelligent Recognition

scholarly journals Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 14
Author(s):  
Mei Dong ◽  
Hongyu Wu ◽  
Hui Hu ◽  
Rafig Azzam ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Monitoring System ◽  
Slope Instability ◽  
Mountainous Area ◽  
Probabilistic Forecasting ◽  
Intelligence Algorithm ◽  
Good Prediction Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Steep Slopes

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

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