Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Han, Xu; Li, Dingkang; Huang, Lizong; Huang, Hanqing; Yang, Jiazhong; Zhang, Yilei; Wu, Xuewei; Lu, Qijing

doi:10.3390/electronics9091367

Cited by 23 publications

(8 citation statements)

References 25 publications

(39 reference statements)

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“…For different data sets, the model needs to be adjusted accordingly. e training and detection inputs of the model are the CSV regularized table data of the corresponding data set [25]. e experimental running environment of the diagnostic model is supported by the TensorFlow framework, and the whole process is implemented by Python programming.…”

Section: Lab Environmentmentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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In order to meet the new requirements of fault diagnosis response and intelligent degree in the current computer network, a fault detection of computer communication network based on an improved neural network algorithm is proposed. First, from the perspective of deep learning, based on the KDD99 data set, the network fault diagnosis method based on the convolutional neural network model is studied, and the data conversion operation of grayscale matrixed raw data is proposed. And experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out, and the establishment of the entire fault diagnosis model is completed. The experimental results show that, in the diagnostic model designed in this paper, the Tanh activation function is used in the first fully connected layer to achieve the best convergence speed. During the training process, it can start to converge after about 24 iterations, and the accuracy rate of the model training process can reach 98.1%, verifying the correctness and superiority of the algorithm and model.

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Section: Lab Environmentmentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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“…However, protections based on time-domain signatures require acquiring data, training the protection and defining threshold values, thus limiting their widespread use [70], [75]. Mathematical methods such as artificial neural networks [74], [79], [80], Kalman filters [81] or fuzzy logic [72], [82] have been applied to identify the specific patterns of the arcs. Another possibility is to apply frequency-domain signatures, such as the harmonic content of the fault current [83], but fault current harmonic frequencies can be masked by those triggered by other faults [84].…”

Section: B State Of the Art Methods To Detect Arcing Faultsmentioning

confidence: 99%

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Riba¹,

Moreno-Eguilaz²,

Ortega³

2022

IEEE Trans. Instrum. Meas.

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Arc faults are serious discharges, damaging insulation systems and triggering electrical fires. This is a transversal topic, affecting from residential to aeronautic applications. Current commercial aircrafts are being progressively equipped with arc fault protections. With the development of more electric aircrafts (MEA), future airliners will require more electrical power to enhance fuel economy, save weight and reduce emissions. The ultimate goal of MEAs is electrical propulsion, where fault management devices will have a leading role, because aircraft safety is of utmost importance. Therefore, current fault management devices must evolve to fulfill the safety requirements of electrical propelled aircrafts. To deal with the increased electrical power generation, the distribution voltage must be raised, thus leading to new electrical fault types, in particular arc tracking and series arcing, which are further promoted by the harsh environments typical of aircraft systems, i.e., low pressure, extreme humidity and a wide range of temperatures. Therefore, the development of specific electrical protections which are able to protect against these fault types is a must. This paper reviews the state-of-theart of electrical protections for aeronautic applications, identifying the current status and progress, their drawbacks and limitations, the future challenges and research needs to fulfill the future requirements of MEAs, with a special emphasis on series arc faults due to arc tracking, because of difficulty in detecting such low-energy faults in the early stage and the importance and harmful effects of tracking activity in cabling insulation systems. This technological and scientific review is based on a deep analysis of research and conference papers, official reports, white papers and international regulations.

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“…This consisted experimental data for learning different relation for the parameters of input and output. Training was given to the data obtained from the model having predicted PV [41].…”

Section: Algorithmmentioning

confidence: 99%

6th International Online Conference on Electronics, Materials Engineering & Nano-Technology, IEMENTech 2022

Taki¹,

Chakrabarty²,

Sarkar³

et al. 2023

6th International Online Conference on Electronics, Materials Engineering &Amp; Nano-Technology, IEMENTech 2022

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Security is of paramount importance in homes, schools, offices and industries. Traditional mechanisms such as locks and keys, door chains etc. have all shown their limitations. These limitations include: the key can break in the lock; the lock can be defective; the key can be stolen or fraudulently duplicated resulting in unauthorized access to a building. This article describes the development of a system to secure access to a building. In this system, users who are authorized to access the building are identified by their radio frequency identification (RFID) card and authenticated by a password. This system provides an effective means of controlling the entry and exit of authorized persons, as well as preventing the entry of unauthorized users and reporting intrusion attempts. To set up this project, we need to acquire a radio frequency identification (RFID) reader, an RFID module, an LCD screen, an ESP8266 MCU node, a database manager and a website.

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Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Cited by 23 publications

References 25 publications

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

6th International Online Conference on Electronics, Materials Engineering & Nano-Technology, IEMENTech 2022

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