scholarly journals NB-IoT based method for monitoring the tilt status of transmission towers

2021 ◽  
Vol 2108 (1) ◽  
pp. 012033
Author(s):  
Yan Zheng ◽  
Yanfeng Wu ◽  
Jianchuan Gao ◽  
Shu Cui
Keyword(s):  
Comparison Method ◽  
Base Station ◽  
Sensor Data ◽  
Transmission Tower ◽  
State Monitoring ◽  
Monitoring Method ◽  
Practical Applications ◽  
Transmission Towers ◽  
Lstm Network ◽  
Upper Computer

Abstract The current monitoring of transmission tower tilt status mainly uses a combination of manual and sensor data collection, which not only has low monitoring efficiency and high cost, but also makes it difficult to guarantee the monitoring accuracy. In response to the problems of the above research, the NB-IoT-based transmission pole tower tilt state monitoring method will be studied. Six-axis attitude sensor GY-521, wind sensor and tension sensor are used to collect the pole tower status data. The NB-IoT network is designed to transmit the noise reduction processed data to the base station, which is transmitted to the upper computer. The data is analyzed in the upper computer using LSTM network to get the monitoring results of the tilt status of the pole tower. The experimental results show that the maximum root mean square error of the proposed monitoring method is only 0.02701, which is much smaller than the comparison method, and the monitoring efficiency is high and the performance is more suitable for practical applications.

scholarly journals Research on Monitoring System of Transmission Tower Operation State Based on Multi-Sensor Data Fusion

2021 ◽  
Vol 2143 (1) ◽  
pp. 012014
Author(s):  
Pengjie He ◽  
Yanwu Dong ◽  
Yu Yan ◽  
Jie Li ◽  
Ziqiang Lu
Keyword(s):  
Data Fusion ◽  
Monitoring System ◽  
Sensor Data ◽  
Normal Operation ◽  
Network System ◽  
Main Role ◽  
Power Network ◽  
Transmission Tower ◽  
State Monitoring ◽  
Modest Contribution

Abstract In the power network system, the main role of transmission tower is responsible for supporting the transmission line, to ensure the normal operation of the power network system. But the transmission tower in the outdoor, easy to be affected by the external environment, resulting in tower collapse, wire broken and other problems. Based on this, this paper, by using multi-sensor data melting technology on transmission tower operation monitoring system are studied, in particular, the system has carried on the design of hardware, software, and then with the inspection of transmission tower temperature and humidity data fusion, the effective monitoring results, confirmed the feasibility of the system design. Through this research, the aim is to make a modest contribution to the transmission tower motion state monitoring.

Ultrasonic unilateral double-position excitation lamb wave defect detection and quantification method for ground electrode of transmission tower

2021 ◽  
pp. 1-15
Author(s):  
Kuan Ye ◽  
Kai Zhou ◽  
Ren Zhigang ◽  
Ruizhe Zhang ◽  
Chunsheng Li ◽  
...  
Keyword(s):  
Guided Waves ◽  
Power Transmission ◽  
Geometric Model ◽  
Guided Wave ◽  
Quantization Error ◽  
Dispersion Function ◽  
Stable Operation ◽  
Transmission Tower ◽  
Ultrasonic Guided Wave ◽  
Transmission Towers

The power transmission tower’s ground electrode defect will affect its normal current dispersion function and threaten the power system’s safe and stable operation and even personal safety. Aiming at the problem that the buried grounding grid is difficult to be detected, this paper proposes a method for identifying the ground electrode defects of transmission towers based on single-side multi-point excited ultrasonic guided waves. The geometric model, ultrasonic excitation model, and physical model are established, and the feasibility of ultrasonic guided wave detection is verified through the simulation and experiment. In actual inspection, it is equally important to determine the specific location of the defect. Therefore, a multi-point excitation method is proposed to determine the defect’s actual position by combining the ultrasonic guided wave signals at different excitation positions. Besides, the precise quantification of flat steel grounding electrode defects is achieved through the feature extraction-neural network method. Field test results show that, compared with the commercial double-sided excitation transducer, the single-sided excitation transducer proposed in this paper has a lower defect quantization error in defect quantification. The average quantization error is reduced by approximately 76%.

scholarly journals Collapse Analysis of a Transmission Tower-Line System Induced by Ice Shedding

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiaxiang Li ◽  
Biao Wang ◽  
Jian Sun ◽  
Shuhong Wang ◽  
Xiaohong Zhang ◽  
...  
Keyword(s):  
Finite Element ◽  
Transmission Lines ◽  
Progressive Collapse ◽  
Element Model ◽  
Transmission Tower ◽  
Line System ◽  
Finite Element Software ◽  
Transmission Towers ◽  
Ice Shedding ◽  
The Impact

Ice shedding causes transmission lines to vibrate violently, which induces a sharp increase in the longitudinal unbalanced tension of the lines, even resulting in the progressive collapse of transmission towers in serious cases, which is a common ice-based disaster for transmission tower-line systems. Based on the actual engineering characteristics of a 500 kV transmission line taken as the research object, a finite element model of a two-tower, three-line system is established by commercial ANSYS finite element software. In the modeling process, the uniform mode method is used to introduce the initial defects, and the collapse caused by ice shedding and its influencing parameters are systematically studied. The results show that the higher the ice-shedding height is, the greater the threat of ice shedding to the system; furthermore, the greater the span is, the shorter the insulator length and the greater the dynamic response of the line; the impact of ice shedding should be considered in the design of transmission towers.

scholarly journals Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiming Zhang ◽  
Yu Yang ◽  
Wei Yang ◽  
Fuying Wu ◽  
Ping Li ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evaluation Model ◽  
Original Data ◽  
Base Station ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Trust Value

The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.

scholarly journals RANCANG-BANGUN SISTEM FLIGHT-RECORDER SEDERHANA UNTUK PELUNCURAN ROKET

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Wahyu Widada
Keyword(s):  
Good Condition ◽  
Base Station ◽  
Sensor Data ◽  
Telemetry System ◽  
Rocket Launch ◽  
Analog Channels ◽  
Important System

In the rocket launch campaign, the telemetry system is very important system in order to send various data from the sensor to the base-station. However the signal of telemetry is not usually in good condition, and the data is very dificult to analyze. In this paper described the method to backup the sensor data by use of flight-recorder. It was very useful if the recovery of payload was successful, and the data flight can be analyze. The system used 2 wire serial eeprom with 131.071 byte of memory and 6 input for analog channels. The speed recording was 10 ms or 100 data per second.

scholarly journals Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 610 ◽  
Author(s):  
Hua Wei ◽  
Hong Luo ◽  
Yan Sun
Keyword(s):  
Target Location ◽  
Geometric Model ◽  
Back Propagation ◽  
Base Station ◽  
Edge Computing ◽  
Computation Offloading ◽  
Target Area ◽  
Mobile Edge Computing ◽  
Trajectory Data ◽  
Practical Applications

The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

scholarly journals Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 203 ◽  
Author(s):  
Jun Long ◽  
Wuqing Sun ◽  
Zhan Yang ◽  
Osolo Ian Raymond
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Time Window ◽  
Research Problem ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Practical Applications ◽  
Long Time ◽  
Benchmark Datasets ◽  
High Level

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.

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