Fault Detection Based on Hierarchical Cluster Analysis in Wide Area Backup Protection System

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis to search after the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact detection of fault components and fault sections, and finally accomplish fault isolation. The facts show that the fault detection of fault component (fault section) can be performed successfully by hierarchical cluster analysis and calculation. The results of hierarchical cluster analysis are accurate and reliable, and the dendrograms of hierarchical cluster analysis are in intuition.

Islanding Protection and Islanding Detection in Low Voltage CIGRE Distribution Network with Distributed Generations

The Power Quality (PQ), security, reliability etc., are the prime objectives of the power system. The protection is developed in such a way that it should be selective, fast, reliable and the cost effective. The study about the islanding protection in Low Voltage (LV) CIGRE distribution and networks like this has been proposed in this paper. This is achieved by developing the protection against the short circuit faults which might appear at the Medium Voltage (MV) bus. The protection of the network with significant penetration of the Distributed Generations (DGs) is a complicated process. The DG units which are directly connected to the grid such as synchronous or induction generators contribute large short power, whereas the DG units which are connected to the grid via inverters carry small amount of the short circuit power. This creates the problems in the protection of the network. If the proper protection coordination measures have not been taken, it might cause the mal-function of the protection devices which put the portion of the power network into the security threats. The selection of the islanding protection devices in this paper is made to protect the network against bi-directional currents at the time of short circuit fault. The LV CIGRE distribution network will enter into islanding if a fault is cleared at the MV bus by the proposed islanding protection devices. It is therefore, essential to detect the islanding in the CIGRE power network. The detection of the island in this network is another major objective of this paper. The detection of the island is proposed by using the technique which is based on the voltage phase angle difference. The simulations are carried out by using DIgSILENT power factory software version 15.0.

Blackout Prevention by Power Swing Detection and Out-of-Step Protection

Power swing evoked by sudden changes like faults or switching operations will become more and more important for protective relaying, due to the growing load flow in electrical power networks. Unwanted trips of the distance protection function must be avoided to prevent cascading effects and blackouts in the network. Selective out-of-step-tripping is required to stop unstable power swing and to prevent damage to affected generators. Therefore a reliable method for detection of power swing is presented, which requires no settings for operation. Power swing can be detected from 0.1 Hz up to 10 Hz swing frequency, also during open pole condition and during asymmetrical operation. A blocking logic prevents unselective trips by the distance protection. However, faults that occur during a power swing must be detected and cleared with a high degree of selectivity and dependability. For unstable power swing a flexible out of step tripping function will be proposed. The coordination of power swing detection, distance protection and out of step protection provides a reliable system protection.

Fault Detection and Protection System of Electric Railway Substation

The industrial power systems are moving to digital networks,and unmanned systems are increasing recently with the technological development of information and communication technology.In particular,reliability of system operation is very important for stable power supply.Most industrial power distribution facilities use a 154 kV or 22.9 kV extra HV(high voltage)power system,and a special monitoring system is required because a large loss occurs when an error occurs in the power facility in the field.Although digital protection relay(IED;intelligent electronic device)and FR(fault recorder)are applied to the field to protect the power system,various improvement activities are still ongoing.In this paper,a 22.9 kV high-voltage panel and its test bed,which are widely used in railway stations,tunnels,substations and large plants,were tested as test subjects.The developed fault detection system automatically starts the AL(auto-lock)circuit or outputs an alarm signal to user by monitoring in real time problems such as CT(current transformer)-open and poor-contact that the existing digital protection relay cannot cover.In addition,it is intended to increase the safety and reliability of the power system by blocking various accidents in the system in advance and accurately analyzing the causes of faults with the developed fault detection system.

Design and implementation of self-protection agent for network-based intrusion detection system

Static secure techniques, such as firewall, hierarchy filtering, distributed disposing,layer management, autonomy agent, secure communication, were introduced in distributed intrusion detection. The self-protection agents were designed, which have the distributed architecture,cooperate with the agents in intrusion detection in a loose-coupled manner, protect the security of intrusion detection system, and respond to the intrusion actively. A prototype self-protection agent was implemented by using the packet filter in operation system kernel. The results show that all the hosts with the part of network-based intrusion detection system and the whole intrusion detection system are invisible from the outside and network scanning, and cannot apperceive the existence of network-based intrusion detection system. The communication between every part is secure. In the low layer, the packet streams are controlled to avoid the buffer leaks exist ing in some system service process and back-door programs, so as to prevent users from misusing and vicious attack like Trojan Horse effectively.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

New Principle for Grounding Fault Feeder Detection in MV Distributions with Neutral Ineffectively Earthed

A new principle for grounding fault feeder detection based on negative sequence current variation and energy dissipated in the fault point is presented. It has high precision in both isolated systems and resonance earthed systems, even in full compensated systems. And it can be installed at the local control unit of feeder in distribution automation systems, such as field terminal unit (FTU). This principle is verified by EMTP simulator and experimentation.

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

A New Method of Early Short Circuit Detection

To reduce the pressure on contacts and circuit breaker and realize the zone selective interlocking (ZSI) function above the instantaneous protection threshold (e.g., >10In), the short circuit current needs to be early detected. The state-of–art of early short circuit detection (ESCD) method is reviewed. Based on the equivalent model of the short circuit, a new method based on the current and its integration is proposed. The prospective current value can be detected in the early stage of the short circuit. According to the evaluation result, the short circuit current can be early forecasted with the proposed method.

Video-based construction vehicles detection and its application in intelligent monitoring system

While vehicle detection on highways has been reported before, to the best of our knowledge, intelligent monitoring system that aims at detecting hydraulic excavators and dump trucks on state-owned land has not been explored thoroughly yet. In this paper, we present an automatic, video-based algorithm for detecting hydraulic excavators and dump trucks. Derived from lessons learned from video processing, we proposed methods for foreground detection based on an improved frame difference algorithm, and then detected hydraulic excavators and dump trucks in the respective region of interest. From our analysis, we proposed methods based on inverse valley feature of mechanical arm and spatial-temporal reasoning for hydraulic excavator detection. In addition, we explored dump truck detection strategies that combine structured component projection with the spatial relationship. Experiments on real-monitoring sites demonstrated the promising performance of our system.

An Intelligent Approach for Intrusion Detection in Industrial Control System

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions.In recent years,there has been a lot of focus on the security of industrial control systems.Due to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically increased.Because they are so inextricably tied to human life,any damage to them might have devastating consequences.To provide an efficient solution to such problems,this paper proposes a new approach to intrusion detection.First,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning process.Then,a prior estimation of the class is proposed based on a support vector machine.Simulation results show that the proposed approach has better anomaly detection performance than existing algorithms.

Development and Application of Lightning Protection Technologies in Power Grids of China

In recent years,along with the rapid expansion of power grids,the increasing complication of grid structures,and the development of smart grids and energy technologies,the lightning protection of power grids becomes increasingly prominent.Power grids of China have acquired significant achievements on lightning protection technologies,which are reviewed in this paper.A technical route of lightning protection is introduced in detail; it allows us to find problems through detection and measurement of lightning,to analyze problems through evaluation and simulation of lightning,to solve problems through lightning protection measures,and to prevent problems through hazard risk warning of lightning.Following the route,the technical breakthroughs of these four aspects in China are presented,including the chinese lightning detection network(CLDN),natural lightning observations,lightning faults detection at transmission lines,lightning current measurements,progresses in lightning distribution maps,lightning fault replays,lightning hazard risk evaluations,and lightning simulated experiments,as well as novel lightning protection measures.The practical devices and systems corresponding to the technologies mentioned above are also introduced and discussed.Due to the progress of lightning protection technologies in recent years,despite the rapidly growing length of transmission lines in China,the lightning accident rate is controlled at a certain level.

Theoretical and experimental assessment of a novel method to establish the complete measurement range of the calorimeter and its limit of detection and quantification

For determining the accuracy of a calorimeter over the instrument’s entire measuring range,a novel method has been established.For this new approach,(a)benzoic acid(C_(6)H_(5)CO_(2)H) as a certified reference material(CRM),(b)SiO_(2) and(c)a mixture of CRM benzoic acid and SiO_(2) have been used.To illustrate the essential difference between 1)the novel analytical method for control of the entire measurement range and 2)the calorimeter calibration,both applications of benzoic acid(BA)have been demonstrated.An experimental result showed that BA was successfully used to check the whole calorimeter measurement range.The results also showed that the same new method was successfully applied to determine the limit of detection and quantification.A new instrument testing process and a new measurement technique have thus been established.In this way,the cost of using CRM to control the accuracy of measuring the entire measuring range of the calorimeter,as shown in this paper,is minimized.The requirements of the ISO/IEC 17025:2017 standard are satisfied.ISO/IEC 17025:2017,together with ISO 9001:2015(quality management systems),ISO 14001:2015(relate to environmental protection)and ISO45001:2018(occupational safety),constitute an integrated quality system by which a testing laboratory may also accredit.

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

Development and Evaluation of an Optical Sensing System for Detection of Herbicide Spray Droplets

Real time monitoring of herbicide spray droplet drift is important for crop production management and environmental protection. Existing spray droplet drift detection methods, such as water-sensitive paper and tracers of fluorescence and Rubidium chloride, are time-consuming and laborious, and the accuracies are not high in general. Also, the tracer methods indirectly quantify the spray deposition from the concentration of the tracer and may change the drift characteristics of the sprayed herbicides. In this study, a new optical sensor system was developed to directly detect the spray droplets without the need to add any tracer in the spray liquid. The system was prototyped using a single broadband programmable LED light source and a near infrared sensor containing 6 broadband spectral detectors at 610, 680, 730, 760, 810, and 860 nm to build a detection system for monitoring and analysis of herbicide spray droplet drift. A rotatory structure driven by a stepper motor in the system was created to shift the droplet capture line going under the optical sensor to measure and collect the spectral signals that reflect spray drift droplets along the line. The system prototype was tested for detection of small (Very Fine and Fine), medium (Medium), and large (Coarse) droplets within the droplet classifications of the American Society of Agricultural and Biological Engineers. Laboratory testing results indicated that the system could detect the droplets of different sizes and determine the droplet positions on the droplet capture line with 100% accuracy at the wavelength of 610 nm selected from the 6 bands to detect the droplets.

Pilot protection of hybrid MMC DC grid based on active detection

Considering the advantages and limitations of traditional identification method,combined with the strategy of active detection,the principle of DC grid pilot protection based on active detection is proposed to improve the sensitivity and reliability of hybrid MMC DC grid protection,and to ensure the accurate identification of fault areas in DC grid.By using the DC fault ride-through control strategy of the hybrid sub-module MMC,the fault current at the converter station DC terminal is limited.Based on the high controllability of hybrid MMC,sinusoidal fault detection signals with the same frequency are injected into the line at each converter station.Based on model recognition,the capacitance model condition is satisfied by the detected signals at both ends during external faults whereas not satisfied during internal faults.The Spearman correlation coefficients is then introduced,and the correlation discriminant of capacitance model is constructed to realize fault area discrimination of DC grid.The simulation results show that the active detection protection scheme proposed in this paper can accurately identify the fault area of DC grid,and is not affected by fault impedance and has low sampling rate requirement.

Applications of graphene-based materials in environmental protection and detection

Many efficient adsorbents and sensors based on graphene and functionalized graphene have been constructed for the removal and detection of environmental pollutants due to its unique physicochemical properties. In this article, recent research achievements are reviewed on the application of graphene-based materials in the environmental protection and detection. For environmental protection, modified graphene can adsorb heavy metal ions in a high efficiency and selectivity, and thus reduces them to metals for recycling. High adsorption capacity of graphene-based materials to kinds of organic pollutants in water was also presented. Several graphene-based sensors with high limit of detection were reported to detect heavy metal ions, toxic gases and organic pollutants in environment. Finally, a perspective on the future challenge of adsorbents and detection devices based on graphene is given.

Quench detection and protection system design and analysis of the 7 T superconducting magnet

A NbTi 7 T superconducting magnet with a 338 mm bore system was rebuilt and the first experiment was carried out in Jan. 2009. The balancing-bridge method used for the quench detection system is analyzed and an improvement of the principle of balancing-bridge conformation is given. The inductances of the coils axe calculated to estimate the proportion of the bridge arms. The appropriate parameters axe selected for the balancing-bridge.

Dynamics of Forest Landscape Transition across Protected Areas in the Niger Delta from 1986 to 2014

The overall aim of this study was to conduct a spatially explicit study of forest cover changes across 13 selected protected areas (PAs) across the study area using a combination of satellite remote sensing and ground data. The objectives of the study included: determine the spatial extent of forest landscape for selected PAs using three epochs of satellite imagery (1986, 2000 and 2014);determine the spatial extent and rates of forest transition over two 14-year periods (1986 to 2000 and 2000 to 2014);and provide a comprehensive summary of current forest landscape status in the study area. The results indicated that the spatial extent of forest landscape for the 13 FRs was 522,164, 481,870 and 457,651 hectares in 1986, 2000 and 2014 respectively. The forest cover transition for the two 14-year periods investigation showed a larger percentage of forest landscape across the 13 FRs were more affected by deforestation in comparison to afforestation. The overall annual deforestation rates for the PAs were 0.82% and 0.68% for 1986 to 2000 and 2000 to 2014 time periods. The annual afforestation rates for both 14-year periods were 0.38% and 0.39% respectively. Based on the results being obtained there was an urgent need to review the ways forest reserves and national parks in Cross River state were managed. The promotion of forest conservation was vital to the preservation of existing forest landscapes and endangered wildlife, such as the Cross River Gorilla gorilla and Nigeria-Cameroon chimpanzees. The role of community participation with the assistance of government and conservation organizations would prove effective in forest monitoring activities across the state. In conclusion, results of this study showed the potential of incorporating satellite remote sensing and ground truth data for better understanding the spatial dynamics of forest cover in PAs across the Niger Delta region.

Protecting the trust and credibility of data by tracking forgery trace based on GANs

With the advent of the 5G Internet of Things era,communication and social interaction in our daily life have changed a lot,and a large amount of social data is transmitted to the Internet.At the same time,with the rapid development of deep forgery technology,a new generation of social data trust crisis has also followed.Therefore,how to ensure the trust and credibility of social data in the 5G Internet of Things era is an urgent problem to be solved.This paper proposes a new method for forgery detection based on GANs.We first discover the hidden gradient information in the grayscale image of the forged image and use this gradient information to guide the generation of forged traces.In the classifier,we replace the traditional binary loss with the focal loss that can focus on difficult-to-classify samples,which can achieve accurate classification when the real and fake samples are unbalanced.Experimental results show that the proposed method can achieve high accuracy on the DeeperForensics dataset and with the highest accuracy is 98%.