Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network

Jianyi Chen, Raj K Aggarwal

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

This paper introduces the design and implementation of a novel phase selection technique using both wavelet transform algorithms and neural network technique to improve the accuracy and efficiency compared with traditional phase selection algorithm under a wide variety of different system and fault conditions. The technique is based on using sharp transitions of current signals generated on the faulted phase. A feature extraction method, based on wavelet transform decomposition, spectral energy extraction and fuzzy logic, is adopted for this work. The algorithm is based on neural network for the decision making part of the scheme. All the test results show that the designed algorithm is very well suited for both accurately classifying fault types and identifying the faulted phase(s) under a wide variety of different system and fault conditions in EHV-transmission lines.
Original languageEnglish
Title of host publication2010 45th International Universities' Power Engineering Conference, UPEC 2010
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages4
ISBN (Electronic)978-0-9565570-2-5
ISBN (Print)978-1-4244-7667-1
Publication statusPublished - Sep 2010
Event2010 45th International Universities' Power Engineering Conference (UPEC) 2010 - Cardiff, UK United Kingdom
Duration: 31 Aug 20103 Sep 2010

Conference

Conference2010 45th International Universities' Power Engineering Conference (UPEC) 2010
CountryUK United Kingdom
CityCardiff
Period31/08/103/09/10

Fingerprint

Wavelet transforms
Electric lines
Neural networks
Fuzzy logic
Feature extraction
Decision making
Decomposition

Cite this

Chen, J., & Aggarwal, R. K. (2010). Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network. In 2010 45th International Universities' Power Engineering Conference, UPEC 2010 [5650116] Piscataway, NJ: IEEE.

Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network. / Chen, Jianyi; Aggarwal, Raj K.

2010 45th International Universities' Power Engineering Conference, UPEC 2010. Piscataway, NJ : IEEE, 2010. 5650116.

Research output: Chapter in Book/Report/Conference proceedingChapter

Chen, J & Aggarwal, RK 2010, Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network. in 2010 45th International Universities' Power Engineering Conference, UPEC 2010., 5650116, IEEE, Piscataway, NJ, 2010 45th International Universities' Power Engineering Conference (UPEC) 2010, Cardiff, UK United Kingdom, 31/08/10.
Chen J, Aggarwal RK. Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network. In 2010 45th International Universities' Power Engineering Conference, UPEC 2010. Piscataway, NJ: IEEE. 2010. 5650116
Chen, Jianyi ; Aggarwal, Raj K. / Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network. 2010 45th International Universities' Power Engineering Conference, UPEC 2010. Piscataway, NJ : IEEE, 2010.
@inbook{eaaa0787b7fd4841971388770e8067d0,
title = "Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network",
abstract = "This paper introduces the design and implementation of a novel phase selection technique using both wavelet transform algorithms and neural network technique to improve the accuracy and efficiency compared with traditional phase selection algorithm under a wide variety of different system and fault conditions. The technique is based on using sharp transitions of current signals generated on the faulted phase. A feature extraction method, based on wavelet transform decomposition, spectral energy extraction and fuzzy logic, is adopted for this work. The algorithm is based on neural network for the decision making part of the scheme. All the test results show that the designed algorithm is very well suited for both accurately classifying fault types and identifying the faulted phase(s) under a wide variety of different system and fault conditions in EHV-transmission lines.",
author = "Jianyi Chen and Aggarwal, {Raj K}",
year = "2010",
month = "9",
language = "English",
isbn = "978-1-4244-7667-1",
booktitle = "2010 45th International Universities' Power Engineering Conference, UPEC 2010",
publisher = "IEEE",
address = "USA United States",

}

TY - CHAP

T1 - Current signal based phase selection in EHV-transmission lines using wavelet transforms and neural network

AU - Chen, Jianyi

AU - Aggarwal, Raj K

PY - 2010/9

Y1 - 2010/9

N2 - This paper introduces the design and implementation of a novel phase selection technique using both wavelet transform algorithms and neural network technique to improve the accuracy and efficiency compared with traditional phase selection algorithm under a wide variety of different system and fault conditions. The technique is based on using sharp transitions of current signals generated on the faulted phase. A feature extraction method, based on wavelet transform decomposition, spectral energy extraction and fuzzy logic, is adopted for this work. The algorithm is based on neural network for the decision making part of the scheme. All the test results show that the designed algorithm is very well suited for both accurately classifying fault types and identifying the faulted phase(s) under a wide variety of different system and fault conditions in EHV-transmission lines.

AB - This paper introduces the design and implementation of a novel phase selection technique using both wavelet transform algorithms and neural network technique to improve the accuracy and efficiency compared with traditional phase selection algorithm under a wide variety of different system and fault conditions. The technique is based on using sharp transitions of current signals generated on the faulted phase. A feature extraction method, based on wavelet transform decomposition, spectral energy extraction and fuzzy logic, is adopted for this work. The algorithm is based on neural network for the decision making part of the scheme. All the test results show that the designed algorithm is very well suited for both accurately classifying fault types and identifying the faulted phase(s) under a wide variety of different system and fault conditions in EHV-transmission lines.

M3 - Chapter

SN - 978-1-4244-7667-1

BT - 2010 45th International Universities' Power Engineering Conference, UPEC 2010

PB - IEEE

CY - Piscataway, NJ

ER -