scholarly journals Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 323 ◽  
Author(s):  
Qiwei Lu ◽  
Zeyu Ye ◽  
Yilei Zhang ◽  
Tao Wang ◽  
Zhixuan Gao
Keyword(s):  
Fault Detection ◽  
Research And Development ◽  
Hardware Implementation ◽  
Detection Method ◽  
Low Cost ◽  
Experimental Methods ◽  
Experimental Results ◽  
National Standard ◽  
Detection Methods ◽  
The Relationship

Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt–ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

RNN-based Fault Detection Method for MMC Photovoltaic Grid-connected System

2021 ◽  
Vol 14 ◽  
Author(s):  
Yuqi Pang ◽  
Gang Ma ◽  
Xiaotian Xu ◽  
Xunyu Liu ◽  
Xinyuan Zhang
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Circuit Breaker ◽  
Detection Methods ◽  
Multilevel Converters ◽  
Modular Multilevel Converters ◽  
Dc Circuit Breaker ◽  
Linear Elements ◽  
Hidden Layer ◽  
Analytical Expressions

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through modular multilevel converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.

A Rapid In Situ Monitoring System for the Determination of Total Phosphorus in Waters

2013 ◽  
Vol 779-780 ◽  
pp. 1526-1531
Author(s):  
Kang Lin Wei ◽  
Ming Chen ◽  
Fei Wang ◽  
Qiong Fang
Keyword(s):  
Monitoring System ◽  
Total Phosphorus ◽  
Detection Method ◽  
Automatic Monitoring ◽  
In Situ Monitoring ◽  
National Standard ◽  
Detection Methods ◽  
Water Quality Parameter ◽  
Automatic Monitoring System

Total phosphorus is an much important key water quality parameter . In view of the technical defects of existing detection methods and instruments for in situ monitoring total phosphorus, a new detection method based on ultrasonic assisted sample digestion and spectrum analysis was put forward in this paper, and the automatic monitoring system prototype based on such detection method had been developed. Aiming at wastewater treatment, the spot experiment had been carried out to contrast prototype with Chinas national standard analysis method for on line measuring total phosphorus in the water, and the results of the comparative experiment showed that the automatic monitoring instrument prototypes had good repeatability (10%) and high accuracy (±10%), which met the technical qualifications of Chinas environmental protection industry standards.

scholarly journals Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 122
Author(s):  
Yang Li ◽  
Fangyuan Ma ◽  
Cheng Ji ◽  
Jingde Wang ◽  
Wei Sun
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Chemical Process ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Normal Operation ◽  
Discriminative Feature ◽  
Inherent Feature ◽  
Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

Fast and Efficient Mutation Detection Method Using Multiplex PCR and Cycle Sequencing

2000 ◽  
Vol 83 (02) ◽  
pp. 244-247 ◽  
Author(s):  
J. M. Costa ◽  
P. Ernault ◽  
D. Vidaud ◽  
M. Vidaud ◽  
D. Meyer ◽  
...  
Keyword(s):  
Multiplex Pcr ◽  
Mutation Detection ◽  
Detection Method ◽  
Deleterious Mutation ◽  
Low Cost ◽  
Detection Methods ◽  
Cycle Sequencing ◽  
Haemophilia B ◽  
Mild Haemophilia

SummaryA method using multiplex PCR followed by cycle-sequencing has been developed to detect mutations in the FIX gene. The procedure was evaluated in 45 severe or mild haemophilia B patients from 45 unrelated families. At least one deleterious mutation was identified in every haemophiliac demonstrating the efficiency of the method. Furthermore the described procedure offers many advantages compared to other screening detection methods: it is fast (less than 48 h), simple (partly automated) and of relatively low cost (it requires only one PCR).

scholarly journals egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5895
Author(s):  
Jiansu Pu ◽  
Jingwen Zhang ◽  
Hui Shao ◽  
Tingting Zhang ◽  
Yunbo Rao
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Communication Networks ◽  
Social Communication ◽  
Detection Method ◽  
Detection Methods ◽  
Visualization System ◽  
Egocentric Network ◽  
Unsupervised Anomaly Detection ◽  
The Relationship

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

Low-cost fault detection method for ECC using Montgomery powering ladder

2011 ◽  
Author(s):  
Duško Karaklajić ◽  
Junfeng Fan ◽  
Jörn-Marc Schmidt ◽  
I Verbauwhede
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Low Cost

Ukrainian arms firms look abroad despite army's needs

2016 ◽  
Keyword(s):  
Research And Development ◽  
Low Cost ◽  
Export Markets ◽  
Content Type ◽  
Technology Transfers ◽  
Manufacturing Sectors ◽  
The Relationship

Significance Ukraine's arms industry was intertwined with Russia's, but the relationship collapsed as conflict broke out in 2014. Despite possessing research and development (R&D) capacity, it relies on old designs and has survived largely by providing repair and maintenance services, although it is trying to explore new export markets. Impacts Continuing conflict will sustain demand for locally-made arms, but only in some manufacturing sectors. Ukraine's exploration of the global arms market will put it in competition with Russia. China, India and others will exploit Ukraine's need for income to obtain low-cost technology transfers.

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