Haar Wavelet-Based Method for Fast Fault Classification in Transmission Lines

Silva, Karolayne Sousa; Dantas, Karcius Marcelus Colaço; Souza, Benemar de; Brito, Núbia Silva Dantas; Costa, F. B.; Silva, J.A.C.B.

doi:10.1109/tdcla.2006.311465

Cited by 22 publications

(6 citation statements)

References 8 publications

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“…ANNs (Artificial neural networks) concept has been recommended for this aim in [25]. Using Wavelet-Based Algorithms, voltage and current signature are proposed for determining the location and type of faults [26]. While the Initial Current Traveling Wave algorithms in [27] are suggested for determining faults and the type of faults by current signature and voltage signature [28].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

A new approach to detecting and classifying multiple faults in IEEE 14-bus system

Alnaib¹,

Alyozbaky²,

Abbawi³

2020

EEJET

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Faults in the power system generally provide considerable changes in its quantities such as under or overpower , over-current, current or power direction, frequency, impedance, and power factor. Reading data related to both currents and voltages is usually involved for detecting and situating the fault on the transmission network. These days, any outage of power in a power grid leads to heavy financial losses for commercial, industrial, and domestic consumers. Random and irregular faults in transmission grids contribute significantly to events of power outages. A significant contribution of this study is a new technique for simulating a multiple simultaneous faults model. The recommended approach is an effective technique for detection, classification and localization of faults in transmission networks of electric power. To attain this objective, a training procedure and a neural network simulation were carried out using m-file in MATLAB. A virtual bus has been proposed to analyze the fault which happens on the transmission line and bus. This technique has been applied on the IEEE 14 bus and multiple simultaneous faults have been mentioned in this study. The fault situations are simulated in m-files through the two-port network performance method, which is a highly enhanced scheme in comparison to the existing methods. The results have been arrived upon by subjecting different buses to varying types of fault. The results provide comprehensive information regarding fault current, post-fault voltages, and fault MVA on all the buses. The values at the bus for voltage, power consumption, and phase angles were specified. As suggested by the findings of the simulation, the proposed methodology is an effective technique for detection, classification and localization of faults

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

A new approach to detecting and classifying multiple faults in IEEE 14-bus system

Alnaib¹,

Alyozbaky²,

Abbawi³

2020

EEJET

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“…In combination with wavelets, which have, as one of the advantages in their favor, the ability to observe transient phenomena [3] and decompose the signal into various scales, making it possible not only to locate a phenomenon in time but to examine the signal in frequency, such a tool gives the user the possibility to collect a signal from nature and visualize it in all its subtleties. The wavelet transform has shown a wide applicability in the field of power systems, from the protection of three-phase generators [4][5][6] or the monitoring of other types of rotating machines through vibration analysis, to the protection of power system elements such as transmission lines [7] and distribution networks [7]. Artificial neural networks have been of great value in terms of the automation of the diagnostic system, making it intelligent [8][9][10], providing an excellent capacity for non-linear mapping and self-learning, efficiently complementing the recognition of failure patterns.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Failures Detection by Vibration Analysis in Rotary Machines Using Wavelet and Artificial Neural Network in the Gera Maranhão Plant.

Vale

Ricciotti

Silva

2022

Int J Innov Educ Res

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This article presents aspects of a tool to assist in predictive maintenance based on vibration analysis in rotating machines using wavelet transform and artificial neural networks. The work analyzed the experimental results of applying a methodology based on the combination of the discrete wavelet transform using a Gaussian window with an artificial neural network for condition monitoring of three-phase induction motors. This approach consisted of simulating faulty and flawless signals using software developed in LabVIEW, their processing, appropriate choice of signals, establishing statistical measures of the chosen signs, and forming the input vectors presented to the artificial neural network. The input vectors are constituted based on statistical measures involving measures of central tendency (mean and centroid), measures of dispersion (RMS value and standard deviation), and a measure of asymmetry (Kurtosis). The most promising configuration was the Multiple Perceptron Layer (MPL) network with four hidden layers containing 256 neurons. Such network showed satisfactory performance for both mechanical failures, with a correct range of around 97%. These results proved to be very effective for detecting mechanical failures, thus being an auxiliary instrument in predictive maintenance.

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“…Fault resistance, occurrence time, location, X/R ratio and phase angle effects were investigated in the simulations. In addition to these studies, fault detection in HV transmission lines [5], fault classification with Haar wavelet transform [6], wavelet-fuzzy combined approach for fault classification and location [7] and many others [8][9][10] can be found in the literature for transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Based Fault Detection and Classification Algorithm for a Real Distribution Feeder

OkumuÅŸ

Nuroğlu

2019

emitter

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As the importance of protection in power systems increase, knowing the type of malfunction occurring in the system has become crucial. Especially in the distribution system where electricity is delivered to the consumer, detecting the right fault type with a short amount of time is important. For this purpose in this study, AkyazÄ±-DÃ¼zkÃ¶y distribution feeder in Trabzon province, where faults commonly occur, is modeled with Digsilent Powerfactory. The model is performed with actual parameters including 465 lines, 243 loads, 233 transformers and 1093 busbars. First, the load flow and short circuit analysis have been carried out for the validation of the model. Then a fault detection and classification algorithm is enhanced using the wavelet transform and the energy of the coefficients. Different types of short circuit faults are created at different points on the model to test the accuracy of the algorithm. The fault inception time and the effect of the fault resistance are also investigated.

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Haar Wavelet-Based Method for Fast Fault Classification in Transmission Lines

Cited by 22 publications

References 8 publications

A new approach to detecting and classifying multiple faults in IEEE 14-bus system

A new approach to detecting and classifying multiple faults in IEEE 14-bus system

Mechanical Failures Detection by Vibration Analysis in Rotary Machines Using Wavelet and Artificial Neural Network in the Gera Maranhão Plant.

Wavelet Based Fault Detection and Classification Algorithm for a Real Distribution Feeder

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