Accurate fault locator for EHV transmission lines based on radial basis function neural networks

Joorabian, Mahmood; Asl, S.M.A. Taleghani; Aggarwal, R.K.

doi:10.1016/j.epsr.2004.02.002

Cited by 76 publications

(36 citation statements)

References 19 publications

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“…In most cases, the input data to ANN is obtained from the analysis of post-fault waveforms. For instance, Fourier transform has been used in [117] to extract the harmonic contents of voltages/currents in order to feed an ANN. However, as mentioned before, FFT cannot be easily used in DC systems due to the lack of time-domain information as well as required time for the process.…”

Section: Signal-processing Based Methodsmentioning

confidence: 99%

Protection of AC and DC distribution systems Embedding distributed energy resources: A comparative review and analysis

Monadi

Zamani

Candela

et al. 2015

Renewable and Sustainable Energy Reviews

125

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Section: Signal-processing Based Methodsmentioning

confidence: 99%

Protection of AC and DC distribution systems Embedding distributed energy resources: A comparative review and analysis

Monadi

Zamani

Candela

et al. 2015

Renewable and Sustainable Energy Reviews

125

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“…This can be carried out on the given feature space for SVM classification. Denoising can also be done using RBF kernel [9].…”

Section: Reconstruction Methodsmentioning

confidence: 99%

“…A particle fault location approach for double circuit lines is introduced and data is extracted from one end of the line [8]. The design and implementation of an artificial neural networks based fault locator for EHV transmission lines are presented in [9]. The location of fault has been presented in the high-voltage power transmission line, support vector machine and frequency characteristics of the measured one-terminal voltage and current transient signals of the systems are reported in [10].…”

Section: Introductionmentioning

confidence: 99%

Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

Gayathri¹,

Kumarappan²

2015

IJCIS

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A new algorithm is developed to enhance the solution for the problems associated with double circuit transmission lines for the mutual coupling between the two circuits under fault conditions and which is highly variable in nature. The algorithm depends on the three-line voltages and the six line currents of double circuit lines at one end. It relies on the application of Support Vector Machine (SVM) and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. Fault resistance, mutual coupling between two circuits and initial prefault conditions are considered. The accuracy of this method has been assessed using a fault simulation software program. In the first state, the accuracy of the method was determined on the basis of SVM reconstructed method. In the second state, this method utilizes voltage and current data acquired at one common end of the two lines. This paper proposes a new hybrid approach for fault location on Extra High Voltage (EHV) lines using RBF based SVM with reconstructed input and Scaled Conjugate Gradient (SCALCG) based neural network method. Sample inputs are determined by MATLAB. The average error fault location in 400kV and 150km line is tested and the results prove that the proposed method is effective and reduces the error within a short duration of time using both RBF based reconstructed input of SVM and SCALCG based neural network.

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“…In [67], a method of accurate fault locator for EHV transmission lines based on radial basis function neural networks is discussed. The locator utilises faulted voltage and current waveforms at one end of the line only.…”

Section: Chapter 2 -Fault In Transmission Cables and Current Fault Lomentioning

confidence: 99%

Online Location of Faults on AC Cables in Underground Transmission Systems

Jensen

2014

Springer Theses

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University in partial fulfilment of the requirements for the PhD degree in Electrical Engineering. The research has been carried out between the 1.09.2010 and the 15.07.2013 at the Department of Energy Technology for Energinet.dk by which I was hired as a PhD-student for the entire project period.The project has been followed full time by two supervisors: Professor Claus Leth Bak (Department of Energy Technology) and Dr. Unnur Stella Gudmundsdottir (Energinet.dk).Energinet.dk has fully funded the research leading to this thesis "Online Location of Faults on AC Cables in Underground Transmission Systems". This funding has been vital for this research project. Travelling to conferences, two visits at forging research institutions, renting of laboratory equipment and performance of and field measurements was made possible thanks to support from the company. During the projet period I supervised two master projects and thought one semester course in power system transients.The scientific papers written as a part of this PhD are included in the back of both the printed version of the thesis as well as in the PDF-file. The papers should not be considered a part of this monograph, but are enclosed if the reader is interested.This thesis has five parts and appendices. An overall literature reference list is presented at the end of the main report. A list of the authored publications is presented at the end of the thesis. Literature references are shown as [i], where i is the number of the literature in the reference list. Tables, figures and equations are shown as C.F, where C is the chapter number and F is a unique number for the figure or table. AcknowledgementsI owe gratitude to many people who helped me in different ways. I would like to thank:• My supervisors, Claus Leth Bak and Unnur Stella Gudmundsdottir for all their help, comments, and many technical discussions during the last three years.• Carl-Erik Madsen, Henrik Kastberg and Carl Willi Hansen for their help with carrying out the field measurements. In the same regards, a very special thanks is extended to Filipe Faria da Silva and Unnur Stella Gudmundsdottir for participating during all measurements.• Filipe Faria da Silva for numerous technical discussions, for reading large parts of the thesis and for many good times in the office.• Rasmus Schmidt Olsen, Unnur Stella Gudmundsdottir, Joachim Holbøll, Per Balle Holst, Poul Erik Pedersen, Thomas Kvarts, Carsten Rasmussen, Claus Leth Bak and Bjarne Søndergaard Bukh for their contributions and comments during the project period.• Everyone at the transmission department at Energinet.dk for their contributions.• Professor Ali Abur for his hospitality and comments to my work during my three month stay at North Eastern University in Boston 2012.• • A special thanks to Dr. Jeewantha Da Silva for numerous discussions, valuable advice and guidance throughout the entire project period.• To Prof. Athula Rajapakse and PhD-student Kasun Nanayakkara both from University of Manitoba, Winnipeg, Manitoba for valuable discuss...

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Accurate fault locator for EHV transmission lines based on radial basis function neural networks

Cited by 76 publications

References 19 publications

Protection of AC and DC distribution systems Embedding distributed energy resources: A comparative review and analysis

Protection of AC and DC distribution systems Embedding distributed energy resources: A comparative review and analysis

Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

Online Location of Faults on AC Cables in Underground Transmission Systems

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