High‐impedance fault detection and classification in power system distribution networks using morphological fault detector algorithm

Kavi, Moses; Mishra, Yateendra; Vilathgamuwa, D.M.

doi:10.1049/iet-gtd.2017.1633

Cited by 53 publications

(25 citation statements)

References 34 publications

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“…Nevertheless, all these works, presents the identification and location of conventional fault without considering the HIF. On the other hand, the authors in and present the identification and location of HIF in power system but fail to test the robustness of the classifier for all other conventional faults and switching transients. In this case, the accuracy of identification of HIF in the system lies in the range of 70% to 100% by methods like DWT, Morphological fault detector and combination of WT with other various classifiers namely Fuzzy‐ARTMAP, Boosted decision tree, Finite element method and Extreme learning machine.…”

Section: Comparison With Literature Workmentioning

confidence: 99%

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Veerasamy

Wahab

Vinayagam³

et al. 2020

Int Trans Electr Energ Syst

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Summary This paper proposes a discrete wavelet transform (DWT)‐based Graphical Language classifier algorithm for identification of high‐impedance fault (HIF) in medium voltage (MV) distribution network of 13.8 kV. The proposed method of classifier is developed using virtual instrumentation LabVIEW facility, for detection of various faults such as symmetrical, unsymmetrical, and HIF in the system. Initially, the MV distribution feeder network has been modeled in MATLAB/Simulink, and the DWT analysis has been carried out with the introduction of various faults in the network to extract the features. The extracted features such as SD and energy values from the fault current signals have been applied to the proposed classifier algorithm to identify the type of fault. The effectiveness of the presented method has been tested and compared with the similar conventional fuzzy‐based approach. The results indicate that the proposed classifier algorithm outperforms to give 100% accuracy, while the fuzzy‐based approach misclassifies the double line to ground fault (LLG), three‐phase fault (LLLG), and HIF. Furthermore, the proposed algorithm with LabVIEW facility is more flexible and can be implemented in real time using data acquisition unit for obtaining fault current signal from power system.

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Section: Comparison With Literature Workmentioning

confidence: 99%

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Veerasamy

Wahab

Vinayagam³

et al. 2020

Int Trans Electr Energ Syst

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“…In 2018, Moses Kavi et al [4] presented a study on the classification of power system disturbances, which include HIFs. Moreover, the MM approach was utilized by the proposed method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the basis of the entropy of each subset, s E represents the information gain in terms of the class partition of its elements is stated in eq. (4).…”

Section: Fault Detection Using Decision Tree Modelmentioning

confidence: 99%

Fault Recognition and Diagnosis Based on a Decision Tree for Power Distribution Systems

Rao¹,

ram²,

Subrahmanyam³

2019

JCMPS

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In recent days, the electrical power system is represented as the complex artificial systems worldwide, as economic and social advancement determined on undamaged, stable, consistent, and economic functions. Due to various arbitrary effects, the unintentional crash happens in electrical power systems. To solve these issues, this paper intends to present the decision tree approach in classifying and detecting fault signals namely sag, transient, and swell in the transmission line. Moreover, the wavelet-decomposed fault signals are extracted and the decision tree is utilized for the diagnoses of fault on the basis of the decomposed signal. Finally, the performance of the proposed approach compares the several existing methods such as SVM, and DBN. The experimental outcomes reveal that the proposed method efficiently notices and classifies the fault signal in the power DS while comparing with the existing methods.

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“…However, the above artificial intelligent (AI) methods using wavelet coefficients are needed to train with the pre-estimated fault conditions. Morphological fault detection algorithms were also introduced in [20,21] using arrival time, polarities and amplitudes of phase voltage and current signals, and polarities of DC component-based method [22]. But these algorithms are complicated to proceed and using polarity to detect faulty phase need to get the signals from both sides of the faulted line.…”

Section: Introductionmentioning

confidence: 99%

A high frequency reflected current signals-based fault type identification method

Myint

Wichakool

2020

IJEECS

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The main objective of this paper is to identify fault type and faulted phase focus on the time delay values of reflected phase and modal current signals. The proposed method identifies fault type with the help of amplitude maxima of detail wavelet coefficient of residual current. The time delay values of phase and modal current reflected signals are used to detect faulty phase instead of using threshold values. Using time delay as a fault type identification parameter is achieved to save the overall protection system operating time because time delay is also the main feature of traveling wave fault location method. Moreover, to ensure the applied wavelet filter, the proposed algorithm is tested with the detail information of the three mother wavelets, such as db4, db6 and db8 and chosen the highest classification accuracy. Various disturbance events were tested with changing different possible fault types, faulted-feeders, fault resistances, fault locations and fault inception times on a loop distribution system. The robustness of the proposed faulted phase selection algorithm is performed through MATLAB Simulation.

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High‐impedance fault detection and classification in power system distribution networks using morphological fault detector algorithm

Cited by 53 publications

References 34 publications

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Fault Recognition and Diagnosis Based on a Decision Tree for Power Distribution Systems

A high frequency reflected current signals-based fault type identification method

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