A Fault Classification and Localization Method for Three-Terminal Circuits Using Machine Learning

Livani, Hanif; Evrenosoğlu, Cansin Yaman

doi:10.1109/tpwrd.2013.2272936

Cited by 111 publications

(49 citation statements)

References 30 publications

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“…AI-based methods [18][19][20][21][22] are those using artificial intelligence techniques such as pattern recognition and machine learning algorithms. Unlike impedance or traveling wave based methods, they do not rely on physics relationships between the measurement and the fault location but rather look for the underlying connection between certain feature of the data and the fault.…”

Section: Ai-based Methodsmentioning

confidence: 99%

Fault Location Using Wide-Area Measurements and Sparse Estimation

Feng

Abur

2016

IEEE Trans. Power Syst.

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Section: Ai-based Methodsmentioning

confidence: 99%

Fault Location Using Wide-Area Measurements and Sparse Estimation

Feng

Abur

2016

IEEE Trans. Power Syst.

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“…On the other hand, harmonic analysis techniques are also used for HIF detection. Scores of researchers have adopted discrete wavelet transform (DWT), wavelet packet transform, tree complex wavelet transform (DT‐CWT) along with machine learning techniques, such as the probabilistic neural network (PNN), artificial neural network (ANN), support vector machine (SVM), etc. for the HIF detection.…”

Section: Introductionmentioning

confidence: 99%

Characterization and classification of high impedance arc fault in distribution system involving metallic and nonmetallic surfaces using empirical mode decomposition

Lala

Karmakar

2019

Int Trans Electr Energ Syst

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Summary The high impedance arc fault (HIAF) poses a significant threat to the living being as it involves arcing. The enormous amount of heat generation during arc is a major concern in this regard. There are different types of arc that may occur depending on the arcing conditions and involved surfaces. The severity of the arc is determined by the involved arcing surface. In this study, arc in metallic (sphere gap, rod gap) and nonmetallic (leaning tree, concrete) surfaces on a distribution system is considered for the analysis. An empirical mode decomposition (EMD)‐based approach is applied along with k‐nearest neighbor (KNN) for the characterization and classification of the real‐time arc of different arcing conditions. The results obtained using EMD and KNN algorithm on arc signals successfully characterize and classify different HIAF by their harmonic signature. Along with KNN, the cross‐validation data‐mining algorithm is also applied to check the robustness of the approach.

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“…In the mainstream literature, many of the researchers have proposed several approaches to detect and localize unsymmetrical fault transients, i.e., expert system, fuzzy logic, optimization technique, artificial neural network (ANN), wavelet analysis, etc. There are some shortcomings with these methods regarding computational speed, accuracy, and reliability of the whole process.…”

Section: Introductionmentioning

confidence: 99%

Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach

Lala

Karmakar

Ganguly

2018

Int Trans Electr Energ Syst

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Summary The paper presents a Stockwell transform (ST) and artificial neural network‐based approach for the detection and localization of faults in distribution systems considering the complexities of network architecture and the distributed generation (DG) integration. Firstly, a faulty‐line detection technique is developed based upon the total harmonic distortion of the fault current signal captured from the line ends. Then, the ST coefficients of the faulty signal are used as an attribute to classify the fault. Finally, the root‐mean‐square values calculated from the ST coefficients are used for the fault localization. The algorithm is tested on an IEEE 13‐bus unbalanced and a 52‐bus practical Indian balanced distribution system with DG. The results are compared with some existing algorithms. The comparisons and the test results show that the proposed technique can be a useful tool in detecting the faults in complicated fault scenarios.

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A Fault Classification and Localization Method for Three-Terminal Circuits Using Machine Learning

Cited by 111 publications

References 30 publications

Fault Location Using Wide-Area Measurements and Sparse Estimation

Fault Location Using Wide-Area Measurements and Sparse Estimation

Characterization and classification of high impedance arc fault in distribution system involving metallic and nonmetallic surfaces using empirical mode decomposition

Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach

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