scholarly journals A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6361
Author(s):  
Mohammad Reza Shadi ◽  
Hamid Mirshekali ◽  
Rahman Dashti ◽  
Mohammad-Taghi Ameli ◽  
Hamid Reza Shaker
Keyword(s):  
Distribution Networks ◽  
Economic Losses ◽  
Equipment Failure ◽  
Power Outages ◽  
Unit Model ◽  
Angle Data ◽  
Two Samples ◽  
Network Fault ◽  
Gated Recurrent Unit ◽  
Linear Nature

Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.

IoT-based Epidemic Monitoring via Improved Gated Recurrent Unit Model

2021 ◽  
pp. 1-1
Author(s):  
Fang Hu ◽  
Jia Liu ◽  
Liuhuan Li ◽  
Mingfang Huang ◽  
Changguo Yang
Keyword(s):  
Epidemic Monitoring ◽  
Unit Model ◽  
Gated Recurrent Unit

Enzootic Activity of Chlamydia in Farms Located in a Hotspot Area for Zoonosis Emergence

2021 ◽  
Author(s):  
Ezequiel Andres Vanderhoeven ◽  
Jessica P. Mosmann ◽  
Adrián Díaz ◽  
Cecilia G. Cuffini
Keyword(s):  
Bird Species ◽  
Farm Animals ◽  
Economic Losses ◽  
Wild Mammals ◽  
Specific Pcr ◽  
Chlamydial Species ◽  
Zoonotic Risk ◽  
Two Samples ◽  
In The Wild ◽  
First Time

Abstract Chlamydias are obligated intracellular Gram-negative bacteria, considered important zoonotic pathogens, broadly present in several bird species and responsible for economic losses in animal production. We analyzed the presence of Chlamydial species with zoonotic risk in farm animals in a highly biodiverse area and with great human circulation, the Argentine, Brazil and Paraguay tri-border area. We surveyed nine farms in an area and nasally swabbed a total of 62 animals. DNA was extracted and specific PCR was performed to identify chlamydial species. We detected Chlamydia spp . in 6.5% (4/62) of the animals tested, positive samples belonged to cattle and none of them showed symptoms of respiratory disease nor had been diagnose with reproductive diseases. Specific nested PCR confirmed two samples belonged to C. pecorum and two to C. psittaci . We report for the first time Chlamydia circulation with zoonotic risk in the region. Surveys in birds and wild mammals could give a better understanding to know what Chlamydial species are circulating in the wild interface. The zoonotic potential should be taking into account as farm workers and the surrounding population could be silent carriers or have respiratory diseases being underdiagnosed, and therefore should be considered in the differential diagnoses.

scholarly journals Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

2021 ◽  
Vol 297 ◽  
pp. 01072
Author(s):  
Rajae Bensoltane ◽  
Taher Zaki
Keyword(s):  
Deep Learning ◽  
Vector Representation ◽  
Rule Based ◽  
External Resources ◽  
Unit Model ◽  
Proposed Model ◽  
Feature Based ◽  
The Impact ◽  
Gated Recurrent Unit ◽  
Machine Learning Models

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.

scholarly journals The Vulnerability of the Power Sector to Climate Variability and Change: Evidence from Indonesia

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3640 ◽  
Author(s):  
Kamia Handayani ◽  
Tatiana Filatova ◽  
Yoram Krozer
Keyword(s):  
Power Plants ◽  
Distribution Networks ◽  
Climate Impacts ◽  
Severe Weather ◽  
Electricity Supply ◽  
Power Sector ◽  
Power Outages ◽  
Lightning Strikes ◽  
Vulnerability To Climate Change ◽  
Weather Events

The power sector is a key target for reducing CO2 emissions. However, little attention has been paid to the sector’s vulnerability to climate change. This paper investigates the impacts of severe weather events and changes in climate variables on the power sector in developing countries, focusing on Indonesia as a country with growing electricity infrastructure, yet being vulnerable to natural hazards. We obtain empirical evidence concerning weather and climate impacts through interviews and focus group discussions with electric utilities along the electricity supply chain. These data are supplemented with reviews of utilities’ reports and published energy sector information. Our results indicate that severe weather events often cause disruptions in electricity supply—in the worst cases, even power outages. Weather-related power outages mainly occur due to failures in distribution networks. While severe weather events infrequently cause shutdowns of power plants, their impact magnitude is significant if it does occur. Meanwhile, transmission networks are susceptible to lightning strikes, which are the leading cause of the networks’ weather-related failures. We also present estimates of financial losses suffered by utilities due to weather-related power disruptions and highlights their adaptation responses to those disruptions.

scholarly journals Factors affecting immunoglobulin concentration in colostrum of healthy Holstein cows immediately after delivery

2011 ◽  
Vol 31 (suppl 1) ◽  
pp. 53-56 ◽  
Author(s):  
Viviani Gomes ◽  
Karina Medici Madureira ◽  
Sérgio Soriano ◽  
Alice Maria Melville Paiva Della Libera ◽  
Maiara Garcia Blagitz ◽  
...  
Keyword(s):  
Holstein Cows ◽  
Economic Losses ◽  
Dairy Production ◽  
Bacteriological Examination ◽  
Agar Gel ◽  
Factors Affecting ◽  
Two Samples ◽  
Newborn Calves ◽  
The Right ◽  
Immunoglobulin Levels

This study analyzed the influence of the number of milkings, number of births, and udder quarter in immunoglobulin (Ig) concentration in the colostrum of healthy Holstein cows. It was collected two samples of colostrum by manual milking, getting the first jets to completion of bacteriological examination and immunoglobulin levels by radial immunodiffusion test in agar gel. Positive samples for bacteriological examination were excluded from this investigation. Medians of immunoglobulin's G, A and M in the colostrum collected before the first and second milking were respectively 9,200 and 6,400mg/dL (p=0.0029); 400 and 200mg/dL (p=0.0018); 800 and 400mg/dL (p=0.0001). Median immunoglobulin concentration in animals that calved once, twice or three times or in cows that calved 4 to 6 times were 6,400; 6,400; 3,200 and 11,200mg/dL IgG; 100, 200, 100 and 800mg/dL IgA ; and 400, 400, 100 and 800mg/dL IgM, respectively. Concentrations of IgG, IgA and IgM were greater in animals that calved more than 4 times (p<0.05). Medians of IgG, IgA and IgM in the right fore quarter (RF), right hind quarter (RH), left fore quarter (LF) and left hind quarter (LH) were, respectively, 7,800; 6,400; 7,800 and 6,400mg/dL; 200, 200, 200 and 200mg/dL; and 400, 400, 400 and 400mg/dL. Ig concentrations in the colostrum of Holstein cows were influenced by the number of milkings after delivery and number of lactations. These variations may be considered risk factors to passive immunity transfer to newborn calves, predisposing them to diseases and causing economic losses to dairy production.

scholarly journals A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna-Based Smart Meters

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Ashraf Ullah ◽  
Nadeem Javaid ◽  
Adamu Sani Yahaya ◽  
Tanzeela Sultana ◽  
Fahad Ahmad Al-Zahrani ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Particle Swarm ◽  
Smart Meters ◽  
Swarm Optimization ◽  
Electricity Theft ◽  
Unit Model ◽  
Gated Recurrent Unit

This paper presents a hybrid model, named as hybrid deep neural network, which combines convolutional neural network, particle swarm optimization, and gated recurrent unit, termed as convolutional neural network-particle swarm optimization-gated recurrent unit model. The major aims of the model are to perform accurate electricity theft detection and to overcome the issues in the existing models. The issues include overfitting and inability of the models to handle imbalanced data. For this purpose, the electricity consumption data of smart meters is taken from state grid corporation of China. An electric utility company gathers the data from the intelligent antenna-based smart meters installed at the consumers’ end. The dataset contains real-time data with missing values and outliers. Therefore, it is first preprocessed to get the refined data followed by feature engineering for selection and extraction of the finest features from the dataset using convolutional neural network. The classification of electricity consumers is performed by dividing them into honest and fraudulent classes using the proposed particle swarm optimization-gated recurrent unit model. The proposed model is evaluated by performing simulations in terms of several performance measures that include accuracy, area under the curve, F 1 -score, recall, and precision. The comparison between the proposed hybrid deep neural network and benchmark models is also performed. The benchmark models include gated recurrent unit, long short term memory, logistic regression, support vector machine, and genetic algorithm-based gated recurrent unit. The results indicate that the proposed hybrid deep neural network model is more efficient in handling class imbalanced issues and performing electricity theft detection. The robustness, accuracy, and generalization of the model are also analyzed in the proposed work.

scholarly journals A Novel Pre-Dispatching Strategy of Power System under Extreme Weather

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2112
Author(s):  
Tianen Huang ◽  
Zhenjie Wu ◽  
Yuantao Wang ◽  
Jian Tang ◽  
Xiang Li ◽  
...  
Keyword(s):  
Distribution Network ◽  
Distribution Networks ◽  
Test System ◽  
Power Grids ◽  
Economic Losses ◽  
Power Outage ◽  
Progressive Hedging ◽  
Progressive Hedging Algorithm ◽  
On Line ◽  
The Cost

Pre-dispatch is an important way for distribution networks to cope with typhoon weather, enhance resilience and reduce economic losses. In order to accurately describe the faults and consequences of components’ failure in the distribution network, this paper establishes a pre-dispatch model to cope with typhoon weather based on line failures consequence analysis. First, Monte Carlo simulation is used to sample the typical fault scenarios of vulnerable lines. According to the location of switchgear, the distribution network is partitioned and a block breaker correlation matrix is established. Combined with the line fault status, a fault consequence model of distribution lines related to the pre-dispatching strategy is established. Then, the objective function is given to minimize the sum of the cost of the pre-dispatch operation and the power outage, and then establish a pre-dispatch model for the distribution network. In order to reduce the computational complexity, PH (Progressive Hedging) algorithm is used to solve the model. Finally, the IEEE-69 test system is used to analyze the effectiveness of the method. The results show that the proposed dispatching model can effectively avoid potential risks, reduce system economic losses and improve the resilience of power grids.

scholarly journals Multiple Leak Detection in Water Distribution Networks Following Seismic Damage

2021 ◽  
Vol 13 (15) ◽  
pp. 8306
Author(s):  
Jeongwook Choi ◽  
Gimoon Jeong ◽  
Doosun Kang
Keyword(s):  
Water Distribution ◽  
Large Scale ◽  
Detection Efficiency ◽  
Leak Detection ◽  
Distribution Networks ◽  
Seismic Damage ◽  
Water Distribution Networks ◽  
Detection Methods ◽  
Economic Losses ◽  
Detection Model

Water pipe leaks due to seismic damage are more difficult to detect than bursts, and such leaks, if not repaired in a timely manner, can eventually reduce supply pressure and generate both pollutant penetration risks and economic losses. Therefore, leaks must be promptly identified, and damaged pipes must be replaced or repaired. Leak-detection using equipment in the field is accurate; however, it is a considerably labor-intensive process that necessitates expensive equipment. Therefore, indirect leak detection methods applicable before fieldwork are necessary. In this study, a computer-based, multiple-leak-detection model is developed. The proposed technique uses observational data, such as the pressure and flow rate, in conjunction with an optimization method and hydraulic analysis simulations, to improve detection efficiency (DE) for multiple leaks in the field. A novel approach is proposed, i.e., use of a cascade and iteration search algorithms to effectively detect multiple leaks (with the unknown locations, quantities, and sizes encountered in real-world situations) due to large-scale disasters, such as earthquakes. This method is verified through application to small block-scale water distribution networks (WDNs), and the DE is analyzed. The proposed detection model can be used for efficient leak detection and the repair of WDNs following earthquakes.

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