Fault Detection and Classification in EHV Transmission Line Based on Wavelet Singular Entropy

He, Zhengyou; Fu, Ling; Lin, Shaohui; Bo, Zhiqian

doi:10.1109/tpwrd.2010.2042624

Cited by 164 publications

(87 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From these families, the most common in power system applications is the Daubechies one. For instance, according to [17], the mother wavelet that is mostly used to fault detection in transmission lines is the Daubechies 4 (db 4). Moreover, Symlet 1 (sym 1) has also been used for classifying transient phenomena in distribution systems [18] and the Coiflet family has been used in harmonic analysis under normal and fault conditions [19].…”

Section: Transient Studies Using Waveletsmentioning

confidence: 99%

“…For instance, Daubechies wavelet 4 (db4) has been applied to fault detection and classification [17] and it is considered the most adequate wavelet for analyzing transient voltage signals, as well as Daubechies 5 (db5) for current signals [20]. Due to this reason, a comparison of both Daubechies wavelets and the proposed one was carried out, and the results are shown in Tab.…”

Section: Daubechies Wavelets Family and Wavelet Etmentioning

confidence: 99%

See 1 more Smart Citation

A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis

Guillén

Idárraga-Ospina

Cortes

2016

Journal of Electrical Engineering

View full text Add to dashboard Cite

Wavelet Transform (WT) is a powerful technique of signal processing, its applications in power systems have been increasing to evaluate power system conditions, such as faults, switching transients, power quality issues, among others. Electromagnetic transients in power systems are due to changes in the network configuration, producing non-periodic signals, which have to be identified to avoid power outages in normal operation or transient conditions. In this paper a methodology to develop a new adaptive mother wavelet for electromagnetic transient analysis is proposed. Classification is carried out with an innovative technique based on adaptive wavelets, where filter bank coefficients will be adapted until a discriminant criterion is optimized. Then, its corresponding filter coefficients will be used to get the new mother wavelet, named wavelet ET, which allowed to identify and to distinguish the high frequency information produced by different electromagnetic transients.

show abstract

Section: Transient Studies Using Waveletsmentioning

confidence: 99%

Section: Daubechies Wavelets Family and Wavelet Etmentioning

confidence: 99%

A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis

Guillén

Idárraga-Ospina

Cortes

2016

Journal of Electrical Engineering

View full text Add to dashboard Cite

show abstract

“…The Wavelet Transform (WLT) has over the last years gained a lot of attention for solving fault location problems on transmission lines [51,52,53,54,55,56,57]. In for instance [58], the Wavelet Transform is used to detect the arrival instance of the fault created travelling wave for an OHL system.…”

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

View full text Add to dashboard Cite

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...

show abstract

“…Among the existing fault detection methodologies in voltage source converter-based high-voltage direct current (VSC-HVDC) transmission systems and conventional AC transmission lines, wavelet analysis is universally applied, especially in combination with entropy theory [14,[16][17][18][19][20], and a boundary condition is applied to distinguish internal from external faults [17,[21][22][23]]. An artificial neural network [24] and machine learning [25,26] are utilized to train the proposed fault detection model or algorithm for improved accuracy.…”

Section: Introductionmentioning

confidence: 99%

Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault

et al. 2018

View full text Add to dashboard Cite

Abstract:The integration of a modular multilevel converter-based high-voltage direct current (MMC-HVDC) transmission system in power networks has led to a high requirement for the rapidity of fault recognition. This study focused on the rapid fault diagnosis of an alternating current (AC) line fault in a back-to-back (BTB) MMC-HVDC system via fault detection and classification. Discrete wavelet transform and modulus maxima were applied to extract the fault features. Phase-mode transformation and normalization were adopted to widen the application range. Simulation and calculation results indicated that the proposed method can detect all fault types in an AC transmission line on the basis of single-side fault information within 1 ms under different values of transition resistance, fault inception angle, and fault distance. The BTB MMC-HVDC model was built using real-time laboratory (RT-LAB) based on the matrix laboratory (MATLAB) software platform, and the fault diagnosis algorithm was performed in MATLAB.

show abstract

Fault Detection and Classification in EHV Transmission Line Based on Wavelet Singular Entropy

Cited by 164 publications

References 24 publications

A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis

A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis

Online Location of Faults on AC Cables in Underground Transmission Systems

Rapid Fault Diagnosis of a Back-to-Back MMC-HVDC Transmission System under AC Line Fault

Contact Info

Product

Resources

About