scholarly journals Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2149
Author(s):  
Noor Hussain ◽  
Mashood Nasir ◽  
Juan Carlos Vasquez ◽  
Josep M. Guerrero
Keyword(s):  
Fault Detection ◽  
Fault Location ◽  
Renewable Energy Sources ◽  
Operation Mode ◽  
Fault Classification ◽  
Power Electronic Converters ◽  
Protection Mechanism ◽  
Advantages And Disadvantages ◽  
Protection Systems ◽  
Recent Developments

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed.

The Method Analysis of Overhead Transmission Line Single-Phase Grounding Fault Detection and Location

2014 ◽  
Vol 1030-1032 ◽  
pp. 1360-1365
Author(s):  
Yun Hong Dai ◽  
Shi Bin Liang ◽  
Ming Yao Hu ◽  
Ying Na Li ◽  
Zhen Gang Zhao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Transmission Line ◽  
Fault Location ◽  
Single Phase ◽  
Small Current ◽  
Location Method ◽  
Overhead Transmission Line ◽  
Advantages And Disadvantages ◽  
Supply And Distribution ◽  
Space Truss

Small current grounding power system is widely used in overhead transmission line in medium voltage distribution network in China. Because of its long lines, branch, space truss structure is complex, low level of automation, It is easily influenced by outside force and the natural environment, and single-phase grounding failure rate is high. It is an important topic to quickly and accurately detect the location of the fault line, and improve the reliability of power supply and distribution system.This paper has carried on the induction and the comparison of various fault detection and location method of overhead transmission line which is currently used in single phase to ground, and analyzed their advantages and disadvantages,given the corresponding conclusions, provided the reference for finding out the reasonable single-phase earth fault location method or improvement.

scholarly journals Analysis of Local Measurement-Based Algorithms for Fault Detection in a Multi-Terminal HVDC Grid

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4808 ◽  
Author(s):  
María José Pérez Molina ◽  
Dunixe Marene Larruskain ◽  
Pablo Eguía López ◽  
Agurtzane Etxegarai
Keyword(s):  
Fault Detection ◽  
Fault Location ◽  
Operating Time ◽  
Rate Of Change ◽  
Local Measurement ◽  
Backup Protection ◽  
High Voltage Direct Current ◽  
Fault Resistance ◽  
Operation Speed ◽  
Protection Systems

One of the most important challenges of developing multi-terminal (MT) high voltage direct current (HVDC) grids is the system performance under fault conditions. It must be highlighted that the operating time of the protection system needs to be shorter than a few milliseconds. Due to this restrictive requirement of speed, local measurement based algorithms are mostly used as primary protection since they present an appropriate operation speed. This paper focuses on the analysis of local measurement based algorithms, specifically overcurrent, undervoltage, rate-of-change-of-current, and rate-of-change-of-voltage algorithms. A review of these fault detection algorithms is presented. Furthermore, these algorithms are applied to a multi-terminal grid, where the influence of fault location and fault resistance is assessed. Then, their performances are compared in terms of detection speed and maximum current interrupted by the HVDC circuit breakers. This analysis aims to enhance the protection systems by facilitating the selection of the most suitable algorithm for primary or backup protection systems. In addition, two new fault type identification algorithms based on the rate-of-change-of-voltage and rate-of-change-of-current are proposed and analyzed. The paper finally includes a comparison between the previously reviewed local measurement based algorithms found in the literature and the simulation results of the present work.

scholarly journals An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Anamika Yadav ◽  
Yajnaseni Dash
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Fault Location ◽  
System Stability ◽  
Fault Classification ◽  
Direction Discrimination ◽  
Artificial Neural ◽  
Transmission Line Protection

Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.

scholarly journals Simultaneous fault detection and diagnosis in electric power system using hybrid method

2018 ◽  
Vol 7 (4) ◽  
pp. 2692
Author(s):  
Dr. Afaneen anwar ◽  
Rana Ali Abttan
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Measurement Method ◽  
Fault Location ◽  
Current Measurement ◽  
Electric Power System ◽  
Fault Detection And Diagnosis ◽  
System Stability ◽  
Fault Classification ◽  
Impedance Method

Simultaneous fault is one of the challenging issues. Faults are the major hurdles in power system designing and protection .Simultaneous fault is the combination of faults indicates that that two or more faults which occur at the same time.The main objective of simultaneous fault detection, classification and location is satisfy accelerates line restoration, maintains system, stability, repairs the fault, decreases the restoration time and increases the system reliability.This paper presents an approach for analysis, detection, classification and location for simultaneous faults in bus bar and transmission line. Two port network is adapted for analysis , voltage and current measurement method is adapted in the fault detection, neural network in the fault classification and location for different types of fault and places were to estimation accurately fault location by analyzing the data available after the beginning of disturbance.All programs were written in MATLAB environment. The programs were test on IEEE- 11 bus bar network. The results clarified that the voltage and current measurement method and impedance method is very effective for simultaneous fault detection, classification and location.  

Method on Fault Detection and Diagnosis for Track Circuit Based on Main Rail Voltage

2014 ◽  
Vol 670-671 ◽  
pp. 1172-1178
Author(s):  
Da Zhuang Wu ◽  
Yu Fang ◽  
Quan Song Ma
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Curve Matching ◽  
Reference Curve ◽  
Diagnosis Method ◽  
Track Circuit ◽  
Detection And Diagnosis

This paper proposes a method of the fault detection and diagnosis for the railway circuit of ZPW-2000 system based on the main track voltage curve. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted. Based on envelope algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Then, the SVM-based fault diagnosis method is used to make sure that the fault classification could be diagnosed intelligently. The experiment results show that the proposed method can accurately identify the track circuit fault state, and the accuracy rate in the diagnosis of the fault location is above 99%, which verify the effectiveness of the method in the fault detection and diagnosis.

scholarly journals Protection Systems for DC Shipboard Microgrids

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5319
Author(s):  
Navid Bayati ◽  
Mehdi Savaghebi
Keyword(s):  
Fault Detection ◽  
Storage Systems ◽  
Comprehensive Review ◽  
Advantages And Disadvantages ◽  
System A ◽  
Fault Behavior ◽  
Protection Systems ◽  
Different Types ◽  
Comprehensive Comparison ◽  
The Impact

In recent years, shipboard microgrids (MGs) have become more flexible, efficient, and reliable. The next generations of future shipboards are required to be equipped with more focuses on energy storage systems to provide all-electric shipboards. Therefore, the shipboards must be very reliable to ensure the operation of all parts of the system. A reliable shipboard MG should be protected from system faults through protection selectivity to minimize the impact of faults and facilitate detection and location of faulty zones with the highest accuracy and speed. It is necessary to have an across-the-board overview of the protection systems in DC shipboards. This paper provides a comprehensive review of the issues and challenges faced in the protection of shipboard MGs. Furthermore, given the different types of components utilized in shipboard MGs, the fault behavior analysis of these components is provided to highlight the requirements for their protection. The protection system of DC shipboards is divided into three sub-systems, namely, fault detection, location, and isolation. Therefore, a comprehensive comparison of different existing fault detection, location, and isolation schemes, from traditional to modern techniques, on shipboard MGs is presented to highlight the advantages and disadvantages of each scheme.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

A hybrid hidden Markov model towards fault detection of rotating components

2016 ◽  
Vol 23 (19) ◽  
pp. 3175-3195 ◽  
Author(s):  
Ayan Sadhu ◽  
Guru Prakash ◽  
Sriram Narasimhan
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Time Scale ◽  
Hidden Markov ◽  
Energy Operator ◽  
Fault Classification ◽  
Multiple Faults ◽  
Condition Indicators ◽  
Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

Voltage Phase Difference Detection for Superconductor Quench

2014 ◽  
Vol 933 ◽  
pp. 82-85
Author(s):  
Shi Feng Wang ◽  
Yi Xiang Yue ◽  
Jin Fang
Keyword(s):  
Phase Difference ◽  
Detection Method ◽  
Phase Detection ◽  
Test Results ◽  
Development Platform ◽  
Advantages And Disadvantages ◽  
Difference Detection ◽  
Protection Systems ◽  
Actual Operation ◽  
Voltage Phase

In this paper, the actual operation of the superconducting motor and electrical parameters, a detailed analysis of the advantages and disadvantages of the existing quench detection method. After that I proposed an innovative detection method - voltage phase difference detection. On this basis, the design phase detection method based on the voltage difference quench detection and protection systems, based on stand-alone test and NI development platform test results, we verify its feasibility of the voltage phase difference detection method, and great superiority.

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