Fault detection, location and classification of a transmission line

Mishra, Debasis; Ray, Papia

doi:10.1007/s00521-017-3295-y

Cited by 95 publications

(32 citation statements)

References 153 publications

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“…A comprehensive review of existing techniques for finding fault location (FL) is provided in [115][116][117]. Fundamentals and new progress in fault location methods based on existing literature are discussed in this paper.…”

Section: Fault Location Finding Methodsmentioning

confidence: 99%

A Review of Fault Diagnosing Methods in Power Transmission Systems

et al. 2020

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Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field. transmission system topologies can be minimized by using the interspersed sensors for the collection of voltage and current signals. The second limitation is the lack of computational capability and communication. Synchronized global positioning system (GPS) sampling and high-speed broadband communications for IEDs in power grids are proposed in [8]. These technical advancements assure the quick response to faulty scenarios and the effective functioning of online monitoring mechanisms based on sensor networks. The availability of high-performance computing solutions gives provision to the implementation of higher computation complexity methods [7].Short circuit faults are more likely to appear in power systems (PS) than the series faults, break in the path of current. Shunt faults result in catastrophes and leave hazardous effects on PS. Short circuit faults can be divided into symmetrical and asymmetrical faults and further classification is presented in Figure 1 for the three-phase system [11].

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Section: Fault Location Finding Methodsmentioning

confidence: 99%

A Review of Fault Diagnosing Methods in Power Transmission Systems

et al. 2020

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“…The present study aims to correct the limitation that occurs in [58], so that consideration and human actions do not affect the detection of the fault. Intelligent fault protection based on a Fuzzy logic system introduced for fault detection in DC MGs, where after the fault is erased, the whole system remains stable [134].…”

Section: A Comparative Study Of Different Methods Of Fault Management In DC Mg Systemsmentioning

confidence: 99%

“…The advantage of the fuzzy logic-based fault detection, identification, and classification is its knowledge representation using the simple statement "IF-Then" relation [134]. The power system's operation in a transient period cannot be described by explicit artificial knowledge; because of many unknown parameters involved and affected the system.…”

Section: ) Fuzzy Logic Based Techniquesmentioning

confidence: 99%

Fault Management in DC Microgrids: A Review of Challenges, Countermeasures, and Future Research Trends

Ali

Terriche

Hoang³

et al. 2021

IEEE Access

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The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed.

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“…The classification itself is implemented in the second step of EEG signal analysis [40]. For signal classification, in particular EEG signal classification, the most commonly used classifiers are SVM [23], [24], [34], kNN [25], [46], Neural Networks [7], [14], [26], [47], Decision Trees [15], [21] and Naïve Bayes Classifier [48].…”

Section: Design Of Approachmentioning

confidence: 99%

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

2020

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The analysis of EEG signal is a relevant problem in health informatics, and its development can help in detection of epileptic's seizures. The diagnosis is based on classification of EEG signal. Different methods and algorithms for classification of EEG signals with an accepted level of reliability and accuracy have been developed over years. All these methods have two steps that are signal preprocessing and classification. The goal of the preprocessing step is removing noise and reduction of the initial signal dimensionality. The signal dimensionality reduction is required by classification methods, but its result is a loss of small information before the classification. In this paper, an approach for EEG signal classification that takes this loss of information into account is considered. The novelty of the considered approach is usage of fuzzy classifier in the classification step. This classifier allows taking uncertainty of initial data into account, which is caused by loss of some information during dimensionality reduction of initial signal. However, application of fuzzy classifier needs modification of the preprocessing step because it requires data in fuzzy form. Therefore, fuzzification procedure is added to the preprocessing step. In this paper, Fuzzy Decision Tree (FDT) is used as the fuzzy classifier for the epileptic's seizure detection. Its application allows achieving 99.5% accuracy of the classification of epileptic's seizure. The comparison with other studies shows that FDT is very effective for task of epileptic's seizure detection.

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Fault detection, location and classification of a transmission line

Cited by 95 publications

References 153 publications

A Review of Fault Diagnosing Methods in Power Transmission Systems

A Review of Fault Diagnosing Methods in Power Transmission Systems

Fault Management in DC Microgrids: A Review of Challenges, Countermeasures, and Future Research Trends

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

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