Wireless Communication Networks and Internet of Things

Zungeru, Adamu Murtala; Subashini, S.; Vetrivelan, P.

doi:10.1007/978-981-10-8663-2

Cited by 7 publications

(15 citation statements)

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“…(1) Combination of Internet of things technology and smart grid Since its successful development, the Internet of things has undergone many significant changes. From the logistics network mainly relying on radio frequency identification technology at the beginning, it has now been studied that a variety of information sensing equipment relying on Internet technology, sensing technology and computer technology can use Internet facilities for information exchange, coordination and processing, Then, it is an interconnection to meet the requirements of information exchange between people and objects in various regions or large areas [5][6]. The application of Internet of things technology in smart grid can realize the perception and exchange of advanced level, convenient operation and unified standard communication information, and complete the distributed intelligent information transmission, measurement and control.…”

Section: Internet Of Thingsmentioning

confidence: 99%

“…The primary problem to be solved by Internet of things technology is information acquisition. How to process information and how to realize overhaul decision should be scientifically planned [9][10]. As an independent management subject, maintenance management is inseparable from the advanced technology management system.…”

Section: Internet Of Thingsmentioning

confidence: 99%

“…(2) Application of power transformer In the contemporary society, electricity is a particularly important energy, it has penetrated into every corner of human society. However, the power generated by power plants usually needs to be transported over long distances to reach the power area [13][14]. When the transmission power is fixed, because the voltage and current are in positive proportion, the current required for transmission decreases with the increase of voltage.…”

Section: Power Transformermentioning

confidence: 99%

“…The decision level fusion in the system uses the extension theory to establish the fault diagnosis model, uses the correlation function to evaluate, and comprehensively diagnoses the transformer fault type [17][18]. The implementation process of decision level data fusion based on extension theory is as follows:…”

Section: Power Transformer Fault Diagnosis System Based On Internet Omentioning

confidence: 99%

“…It can be seen that the Internet of things can well deal with the problem of more distribution terminals and frequent changes. (5) Training sample statistics In this paper, the data of one day to more than one day are collected in the field. In the training model, one transformer data is selected every hour to remove the invalid test point data.…”

Section: Figure 5 Comparison Of Partial Optimized Test Setsmentioning

confidence: 99%

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Power Transformer Fault Diagnosis System Based on Internet of Things

Wang

Liu

Chen

et al. 2020

Preprint

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Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective: To explore the utility of Internet of things in power transformer fault diagnosis system. Methods: A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results: The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.000 l, and it can correctly identify the fault types of the non-training samples. It can be seen that the technical support of the Internet of things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

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Section: Internet Of Thingsmentioning

confidence: 99%

Section: Internet Of Thingsmentioning

confidence: 99%

Section: Power Transformermentioning

confidence: 99%

Section: Power Transformer Fault Diagnosis System Based On Internet Omentioning

confidence: 99%

Section: Figure 5 Comparison Of Partial Optimized Test Setsmentioning

confidence: 99%

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Power Transformer Fault Diagnosis System Based on Internet of Things

Wang

Liu

Chen

et al. 2020

Preprint

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Human fall detection algorithm based on random forest and MPU6050

Zeng,

Xu,

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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In recent years, the health problems of the aged persons have been paid more and more attention in China. Falls are one of the main reasons affecting the health and safety of the aged, especially accidental injuries caused by falls of the aged persons have attracted much attention. In this paper, the STM32F103C8T6 chip and the MPU6050 sensor are used to collect the posture information of the human body in the normal state and the falling state, and the Fast Fourier Transform was performed on the collected posture data to obtain the frequency data of human behavior. The frequency information is used as the dataset for the random forest model. Five-fold cross-validation strategy was used to increase model performance. Besides, different filtering algorithms were utilized and compared. The model accuracy reached94%, which demonstrated that the proposed algorithm in this paper is promising for better human fall detection problem.

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Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network

Ren

2022

Mobile Information Systems

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The design and application of the equipment fault diagnosis system have been improved and upgraded, allowing it to effectively detect the equipment’s operation status and promptly eliminate hidden faults, reducing the occurrence of unexpected accidents and improving the safety index of people’s lives. The purpose of this essay is to design and apply neural network (NN) fault diagnosis system model in power Internet of things (IOT) equipment and explore its accuracy and effectiveness. The BP neural network (BPNN) algorithm was used to construct model of a fault monitoring testing of the power IOT equipment. Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. The network parameters were as follows: there were four input layer nodes, seven hidden layer nodes, and five output layer nodes, the training times were 10000, and the allowable error was 0.002. In this paper, we use the IOT to detect model of a fault monitoring testing of power equipment designed in each sample, the success rate is as high as 97.5%, and the designed network structure and network parameters are reasonable. The trained loss is less than 0.001, and the nontraining set samples may be appropriately identified. It is clear that the NN has a high application for power equipment fault diagnosis in the IOT value.

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Wireless Communication Networks and Internet of Things

Abstract: The books of this series are submitted to ISI Proceedings, EI-Compendex, SCOPUS, MetaPress, Springerlink ** Lecture Notes in Electrical Engineering (LNEE) is a book series which reports the latest research and developments in Electrical Engineering, namely:

Cited by 7 publications

References 0 publications

Power Transformer Fault Diagnosis System Based on Internet of Things

Power Transformer Fault Diagnosis System Based on Internet of Things

Human fall detection algorithm based on random forest and MPU6050

Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network

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