Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs

Hadi Zayandehroodi; Azah Mohamed; Hussain Shareef; Marjan Mohammadjafari

doi:10.1109/PECON.2010.5699422

Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs

Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef, Marjan Mohammadjafari

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

22 Citations (Scopus)

Abstract

With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.

Original language	English
Title of host publication	PECon2010 - 2010 IEEE International Conference on Power and Energy
Pages	341-345
Number of pages	5
DOIs	https://doi.org/10.1109/PECON.2010.5699422
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Power and Energy, PECon2010 - Kuala Lumpur, Malaysia Duration: Nov 29 2010 → Dec 1 2010

Publication series

Name	PECon2010 - 2010 IEEE International Conference on Power and Energy

Conference

Conference	2010 IEEE International Conference on Power and Energy, PECon2010
Country/Territory	Malaysia
City	Kuala Lumpur
Period	11/29/10 → 12/1/10

Keywords

Distributed generation (DG)
Distribution network
Fault location
Multilayer perceptron neural network (MLPNN)
Radial basis function neural network (RBFNN)

ASJC Scopus subject areas

Energy Engineering and Power Technology

Access to Document

10.1109/PECON.2010.5699422

Cite this

Zayandehroodi, H., Mohamed, A., Shareef, H., & Mohammadjafari, M. (2010). Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs. In PECon2010 - 2010 IEEE International Conference on Power and Energy (pp. 341-345). Article 5699422 (PECon2010 - 2010 IEEE International Conference on Power and Energy). https://doi.org/10.1109/PECON.2010.5699422

Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs. / Zayandehroodi, Hadi; Mohamed, Azah; Shareef, Hussain et al.
PECon2010 - 2010 IEEE International Conference on Power and Energy. 2010. p. 341-345 5699422 (PECon2010 - 2010 IEEE International Conference on Power and Energy).

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

Zayandehroodi, H, Mohamed, A, Shareef, H & Mohammadjafari, M 2010, Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs. in PECon2010 - 2010 IEEE International Conference on Power and Energy., 5699422, PECon2010 - 2010 IEEE International Conference on Power and Energy, pp. 341-345, 2010 IEEE International Conference on Power and Energy, PECon2010, Kuala Lumpur, Malaysia, 11/29/10. https://doi.org/10.1109/PECON.2010.5699422

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title = "Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs",

abstract = "With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.",

keywords = "Distributed generation (DG), Distribution network, Fault location, Multilayer perceptron neural network (MLPNN), Radial basis function neural network (RBFNN)",

author = "Hadi Zayandehroodi and Azah Mohamed and Hussain Shareef and Marjan Mohammadjafari",

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PY - 2010

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N2 - With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.

AB - With high penetration of distributed generations (DGs), power distribution system is regarded as a multisource system in which fault location scheme must be direction sensitive. This paper presents an automated fault location method using radial basis function neural network (RBFNN) for a distribution system with DG units. In the proposed method, the fault type is first determined by normalizing the fault currents of the main source and then fault location is predicted by using RBFNN. Several case studies have been considered to verify the accuracy of the RBFNN. A comparison is also made between the RBFNN and the conventional multilayer perceptron neural network for locating faults in a power distribution system with DGs. The test results showed that the RBFNN can accurately determine the location of faults in a distribution system with several DG units.

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KW - Fault location

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Performance comparison of mlp and rbf neural networks for fault location in distribution networks with DGs

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