scholarly journals Islanding Fault Detection in Microgrids—A Survey

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3479 ◽  
Author(s):  
Mehdi Hosseinzadeh ◽  
Farzad Rajaei Salmasi
Keyword(s):  
Fault Detection ◽  
Detection Methods ◽  
Future Research ◽  
Extensive Literature ◽  
Detection Techniques ◽  
Comprehensive List ◽  
Advantages And Disadvantages ◽  
Utility Grid

This paper provides an overview of islanding fault detection in microgrids. Islanding fault is a condition in which the microgrid gets disconnected from the microgrid unintentionally due to any fault in the utility grid. This paper surveys the extensive literature concerning the development of islanding fault detection techniques which can be classified into remote and local techniques, where the local techniques can be further classified as passive, active, and hybrid. Various detection methods in each class are studied, and advantages and disadvantages of each method are discussed. A comprehensive list of references is used to conduct this survey, and opportunities and directions for future research are highlighted.

scholarly journals A Survey of Islanding Detection Methods for Microgrids and Assessment of Non-Detection Zones in Comparison with Grid Codes

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 460
Author(s):  
José Antonio Cebollero ◽  
David Cañete ◽  
Susana Martín-Arroyo ◽  
Miguel García-Gracia ◽  
Helder Leite
Keyword(s):  
Detection Methods ◽  
Normal Operation ◽  
Islanding Detection ◽  
Personal Safety ◽  
Detection Zone ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Grid Codes ◽  
Utility Grid

Detection of unintentional islanding is critical in microgrids in order to guarantee personal safety and avoid equipment damage. Most islanding detection techniques are based on monitoring and detecting abnormalities in magnitudes such as frequency, voltage, current and power. However, in normal operation, the utility grid has fluctuations in voltage and frequency, and grid codes establish that local generators must remain connected if deviations from the nominal values do not exceed the defined thresholds and ramps. This means that islanding detection methods could not detect islanding if there are fluctuations that do not exceed the grid code requirements, known as the non-detection zone (NDZ). A survey on the benefits of islanding detection techniques is provided, showing the advantages and disadvantages of each one. NDZs size of the most common passive islanding detection methods are calculated and obtained by simulation and compared with the limits obtained by ENTSO-E and islanding standards in the function of grid codes requirements in order to compare the effectiveness of different techniques and the suitability of each one.

scholarly journals Recent Advancement in Anomaly Detection in Surveillance Videos

2019 ◽  
Vol 8 (2) ◽  
pp. 964-970
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Video Data ◽  
Detection Methods ◽  
Future Research ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Computer ◽  
Banking Education ◽  
To Come

Detection of Anomaly is of a notable and emergent problem into many diverse fields like information theory, deep learning, computer vision, machine learning, and statistics that have been researched within the various application from diverse domains including agriculture, health care, banking, education, and transport anomaly detection. Newly, numbers of important anomaly detection techniques along with diverseness of sort have been watched. The main aim of this paper to come up with a broad summary of the present development on detection of an anomaly, exclusively for video data with mixed types and high dimensionalities, where identifying the anomalous behaviors and event or anomalous patterns is a significant task. The paper expresses the advantages and disadvantages of the detection methods the experiments tried on the publically available benchmark dataset to assess numerous popular and classical methods and models. The objective of this analysis is to furnish an understanding of recent computer vision and machine algorithms methods and also state-of-the-art deep learnings techniques to detect anomalies for researchers. At last, the paper delivered roughly directions for future research on an anomalies detection.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

Partial Discharge Detection Using Acoustic and Optical Methods in High Voltage Power Equipments: A Review

2013 ◽  
Vol 845 ◽  
pp. 283-286 ◽  
Author(s):  
Malik Abdul Razzaq Al Saedi ◽  
Mohd Muhridza Yaacob
Keyword(s):  
Acoustic Emission ◽  
High Voltage ◽  
Power Transformer ◽  
Partial Discharge ◽  
Optical Detection ◽  
Detection Threshold ◽  
Optical Methods ◽  
Detection Methods ◽  
Detection Techniques ◽  
Advantages And Disadvantages

There is a high risk of insulation system dielectric instability when partial discharge (PD) occurs. Therefore, measurement and monitoring of PD is an important preventive tool to safeguard high-voltage equipment from wanton damage. PD can be detected using optical method to increase the detection threshold and to improve the performance of on-line measurement of PD in noise environment. The PD emitted energy as acoustic emission. We can use this emitted energy to detect PD signal. The best method to detect PD in power transformer is by using acoustic emission. Optical sensor has some advantages such as; high sensitivity, more accuracy small size. Furthermore, in on-site measurements and laboratory experiments, it isoptical methodthat gives very moderate signal attenuations. This paper reviews the available PD detection methods (involving high voltage equipment) such as; acoustic detection and optical detection. The advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages from the consideration of accuracy and suitability for the applications when compared to other techniques.

Material Based Fault Detection Methods for PV Systems

2020 ◽  
Vol 865 ◽  
pp. 111-115
Author(s):  
Khaled Osmani ◽  
Ahmad Haddad ◽  
Thierry Lemenand ◽  
Bruno Castanier ◽  
Mohamad Ramadan
Keyword(s):  
Fault Detection ◽  
Raw Materials ◽  
Raw Material ◽  
Detection Methods ◽  
Critical Aspect ◽  
Pv System ◽  
Detection Techniques ◽  
Pv Systems ◽  
Fault Type ◽  
Pv Cell

The overall efficiency of a PV system is strongly affected by the PV cell raw materials. Since a reliable renewable energy source is expected to produce maximum power with longest lifetime and minimum errors, a critical aspect to bear in mind is the occurrence of PV faults according to raw material types. The different failure scenarios occurring in PV system, decrease its output power, reduce its life expectancy and ban the system from meeting load demands, yielding to severe consecutive blackouts. This paper aims first to present different core materials types, material based fault occurring on the PV cell level and consequently the fault detection techniques corresponding to each fault type.

scholarly journals Prospects for rapid bioluminescent detection methods in the food industry – a review

2011 ◽  
Vol 23 (No. 3) ◽  
pp. 85-92 ◽  
Author(s):  
P. Dostálek ◽  
T. Brányik
Keyword(s):  
Historical Development ◽  
Food Industry ◽  
Food Samples ◽  
Detection Methods ◽  
Rapid Methods ◽  
Detection Techniques ◽  
Atp Assay ◽  
Advantages And Disadvantages ◽  
Bacterial Bioluminescence ◽  
Fluorescent Method

This review surveys rapid bioluminescent detection techniques applied in food industry and discusses the historical development of the rapid methods. These techniques are divided into two groups: methods based on bioluminescent adenosine triphosphate (ATP) assay, and on bacterial bioluminescence. The advantages and disadvantages of these methods are described. The article provides the bibliography of fluorescent method applications in food samples.    

scholarly journals Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3995 ◽  
Author(s):  
Yaoguang Wei ◽  
Yisha Jiao ◽  
Dong An ◽  
Daoliang Li ◽  
Wenshu Li ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Real Time ◽  
Optical Detection ◽  
Oxygen Sensors ◽  
Detection Methods ◽  
Future Research ◽  
Advantages And Disadvantages ◽  
Oxygen Detection ◽  
Detection Technology ◽  
Intelligent Technology

Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.

scholarly journals A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

2021 ◽  
Vol 30 (1) ◽  
pp. 53-78
Author(s):  
Masood Ahmad ◽  
Rosmiwati Mohd-Mokhta
Keyword(s):  
Fault Detection ◽  
Linear Time ◽  
Fault Detection And Isolation ◽  
Estimation Methods ◽  
Future Research ◽  
Practical Applications ◽  
Detection Techniques ◽  
Linear Time Invariant ◽  
Model Based ◽  
Time Invariant

With the ongoing increase in complexity, less tolerance to performance degradation and safety requirements of practical systems has increased the necessity of fault detection (FD) as early as possible. During the last few decades, many research findings have been developed in fault diagnosis that addresses the issue of fault detection and isolation in linear and nonlinear systems. The paper’s objective is to present a survey on various state-of-art model-based FD techniques developed for linear time-invariant (LTI) systems for the interested readers to learn about recent development in this field. Model-based FD techniques for LTI systems are classified as parameter-estimation methods, parity-space-based methods, and observer-based methods. The background and recent progress, in context to fault detection, of each of these methods and their practical applications are discussed in this paper. Furthermore, two different FD techniques are compared via analytical equations and simulation results obtained from the DC motor model. In the end, possible future research directions in model-based FD, particularly for the LTI system, are highlighted for prosperous researchers. A comparison and emerging research topic make this contribution different from the existing survey papers on FD.

scholarly journals Recent Advances in Anomaly Detection Methods Applied to Aviation

Aerospace ◽  
2019 ◽  
Vol 6 (11) ◽  
pp. 117 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Progress of Researches on the Surface Topography Detection Techniques for Grinding Wheel

2013 ◽  
Vol 797 ◽  
pp. 505-510 ◽  
Author(s):  
Wei Liu ◽  
Zhao Hui Deng ◽  
Lin Lin Wan ◽  
Qiao Ping Wu ◽  
Hao Tang
Keyword(s):  
Surface Topography ◽  
Research Work ◽  
Research Direction ◽  
Detection Methods ◽  
Future Research ◽  
Grinding Wheel ◽  
Detection Techniques ◽  
Important Input ◽  
Work Done ◽  
Microscopic Surface

As one of the important input parameters in the grinding process, the surface topography characteristic of grinding wheel has a decisive impact on the grinding performance. To evaluate and analyze the microscopic surface topography, the premise must be able to detect the microscopic surface topography accurately. Based on the measurement of detection device and its spatial position, the surface topography detection methods have been classified, the principle has been specifically analyzed for some typical detection methods, and the research work done by domestic and foreign scholars in the area. Finally, the current problems and future research direction in the field have been analyzed.

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