Fault diagnosis on electrical distribution systems based on fuzzy logic

Ramón Perez; Esteban Inga; Alexander Aguila; Carmen Vásquez; Liliana Lima; Amelec Viloria; Maury Ardila Henry

doi:10.1007/978-3-319-93818-9_17

Fault diagnosis on electrical distribution systems based on fuzzy logic

Ramón Perez, Esteban Inga, Alexander Aguila, Carmen Vásquez, Liliana Lima, Amelec Viloria, Maury Ardila Henry

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

Abstract

The occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.

Translated title of the contribution	Diagnóstico de fallos en sistemas de distribución eléctrica basado en lógica difusa
Original language	English
Title of host publication	Fault diagnosis on electrical distribution systems based on fuzzy logic
Editors	Ying Tan, Qirong Tang, Yuhui Shi
Pages	174-185
Number of pages	12
ISBN (Electronic)	9783319938172
DOIs	https://doi.org/10.1007/978-3-319-93818-9_17
State	Published - 1 Jan 2018
Event	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - , Spain Duration: 1 Jan 2010 → …

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10942 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Country/Territory	Spain
Period	1/01/10 → …

Keywords

Distribution systems
Fault location
Fault type
Fuzzy logic

Access to Document

10.1007/978-3-319-93818-9_17

Cite this

Perez, R., Inga, E., Aguila, A., Vásquez, C., Lima, L., Viloria, A., & Henry, M. A. (2018). Fault diagnosis on electrical distribution systems based on fuzzy logic. In Y. Tan, Q. Tang, & Y. Shi (Eds.), Fault diagnosis on electrical distribution systems based on fuzzy logic (pp. 174-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10942 LNCS). https://doi.org/10.1007/978-3-319-93818-9_17

Perez, Ramón ; Inga, Esteban ; Aguila, Alexander et al. / Fault diagnosis on electrical distribution systems based on fuzzy logic. Fault diagnosis on electrical distribution systems based on fuzzy logic. editor / Ying Tan ; Qirong Tang ; Yuhui Shi. 2018. pp. 174-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Fault diagnosis on electrical distribution systems based on fuzzy logic",

abstract = "The occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.",

keywords = "Distribution systems, Fault location, Fault type, Fuzzy logic",

author = "Ram{\'o}n Perez and Esteban Inga and Alexander Aguila and Carmen V{\'a}squez and Liliana Lima and Amelec Viloria and Henry, {Maury Ardila}",

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doi = "10.1007/978-3-319-93818-9_17",

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Perez, R, Inga, E , Aguila, A, Vásquez, C, Lima, L, Viloria, A & Henry, MA 2018, Fault diagnosis on electrical distribution systems based on fuzzy logic. in Y Tan, Q Tang & Y Shi (eds), Fault diagnosis on electrical distribution systems based on fuzzy logic. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10942 LNCS, pp. 174-185, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Spain, 1/01/10. https://doi.org/10.1007/978-3-319-93818-9_17

Fault diagnosis on electrical distribution systems based on fuzzy logic. / Perez, Ramón; Inga, Esteban ; Aguila, Alexander et al.
Fault diagnosis on electrical distribution systems based on fuzzy logic. ed. / Ying Tan; Qirong Tang; Yuhui Shi. 2018. p. 174-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10942 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Fault diagnosis on electrical distribution systems based on fuzzy logic

AU - Perez, Ramón

AU - Inga, Esteban

AU - Aguila, Alexander

AU - Vásquez, Carmen

AU - Lima, Liliana

AU - Viloria, Amelec

AU - Henry, Maury Ardila

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.

AB - The occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.

KW - Distribution systems

KW - Fault location

KW - Fault type

KW - Fuzzy logic

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Fault diagnosis on electrical distribution systems based on fuzzy logic

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ER -

Perez R, Inga E , Aguila A, Vásquez C, Lima L, Viloria A et al. Fault diagnosis on electrical distribution systems based on fuzzy logic. In Tan Y, Tang Q, Shi Y, editors, Fault diagnosis on electrical distribution systems based on fuzzy logic. 2018. p. 174-185. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93818-9_17

Fault diagnosis on electrical distribution systems based on fuzzy logic

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