Adaptive wavelets applied to fault classification on transmission lines

Pérez, Fabián; Orduña, Eduardo; Guidi, G.

doi:10.1049/iet-gtd.2010.0615

Cited by 40 publications

(19 citation statements)

References 18 publications

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“…These methods use the fault voltage (V f ) as common elements between two circuits (see Equation (20)). Due to this, it is possible to simplify this term (or what is the same, R f value influence), resulting Equation (21).…”

Section: Multi-end Methodsmentioning

confidence: 99%

“…Traditionally, these techniques apply direct analysis over symmetrical components [20] or use spectral analysis (such as FFT [18] or DWT [21]) to extract the fault features. These studies are commonly supported by computational intelligence, such as Artificial Neural Networks (ANNs) [22], Fuzzy Logic (FL) [23], Decision Trees (DTs) [24] or Support Vector Machines (SVMs) [25].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

et al. 2016

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Abstract:In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requirements are in line with the typical instrumentation deployed in distribution networks. This work is completed with an exhaustive analysis of these methods over a PSCAD TM X4 implementation of the standard IEEE Node Test Feeder, which truly allows us to consistently compare the results of these location methods and to determine the advantages and drawbacks of each of them.

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Section: Multi-end Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

et al. 2016

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“…The phase selector may fail to detect a high resistance fault in the transmission line, because the fault-induced transients are very smooth in this situation and the entropy may be always less than the threshold. In order to obtain more reliable phase selection results under various fault conditions, four fault classification indices are defined as (13) where , , , and are the classification indices of the zero-sequence component; phase A, B, and C, respectively; and , , , and are the entropies of the zero-sequence component, phase A, B, and C, respectively.…”

Section: B Classification Indexmentioning

confidence: 99%

“…A novel WT-based technique for phase selection is presented in [10], and the online applications of WT to power system relaying are reported in [11]. The adaptive wavelet algorithm is proposed for feature detection in [12], based on which two methods that use single-phase measurement are presented to classify faults on transmission lines [13], [14]. In these two papers, Bayesian linear discrimination analysis is employed to process the data extracted by WT for fault classification.…”

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confidence: 99%

Morphology Singular Entropy-Based Phase Selector Using Short Data Window for Transmission Lines

Zhang

et al. 2014

IEEE Trans. Power Delivery

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This paper presents a new technique called morphology singular entropy (MSE), based on which a phase selector for transmission lines is developed. MSE combines mathematical morphology, singular value decomposition (SVD), and entropy theory, making it insensitive to noise and easy to extract the features of the fault-induced transients. Voltage signals are used as inputs of the proposed MSE-based phase selector. Each signal is processed by a multiscale morphological filter first, and a matrix consisting of the outputs of the filter is formed. By decomposing the matrix using SVD, the singular values are obtained, and then the entropy in association with this signal can be calculated. Afterwards, in order to improve the sensitivity and reliability of the phase selection, four classification indices derived from the entropies are defined. The phase selection is performed by comparing these four indices to a preset threshold. Simulation data generated using PSCAD/EMTDC and real-life data have been employed to verify the performance of the proposed method.

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“…The wavelet coefficients of a signal, as well as their spectral energy, have been used to detect and classify faults and some power quality (PQ) disturbances [11]- [13]. In power transformer protection, some disturbances as external faults, internal faults and transformer energizing, which present transients, can be also properly analyzed by this tool [5], [8], [14] .…”

Section: Introductionmentioning

confidence: 99%

Differential protection of power transformers using the wavelet transform

Medeiros

Fernandes

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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Several disturbances involving power transformers commonly occur in the Electric Power System. External faults near the transformer, internal faults, as well as transformer energizing and overexcitation are the main events evaluated in the protection of the power transformers. All of the aforementioned events present transients, which can be properly analyzed by the wavelet transform. The Maximal Overlap Discrete Wavelet Transform (MODWT) can be used to detect transients in some power system disturbances. This paper presents an algorithm based on MODWT for detection and identification of external faults near or far the transformer, internal faults, and transformer energization by using differential currents in the wavelet domain. A system of 230 kV was modeled using ATP/EMTP software. The results reveal the advantages of using this type of modeling, since It provides reliability and speed in detection of these events.

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Adaptive wavelets applied to fault classification on transmission lines

Cited by 40 publications

References 18 publications

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

Morphology Singular Entropy-Based Phase Selector Using Short Data Window for Transmission Lines

Differential protection of power transformers using the wavelet transform

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