Method to decide a multi-fault rush repair robust strategy in power distribution networks

Zhao, H; Lu, Z; Zhang, J; Li, X; Guo, X; Sun, X

Method to decide a multi-fault rush repair robust strategy in power distribution networks

Zhao, H Lu, Z Zhang, J

Li, X Guo, X Sun, X

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Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2016, 56 pp. 91 - 101
Issue Date:: 2016-11-01

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Field	Value	Language
dc.contributor.author	Zhao, H	en_US
dc.contributor.author	Lu, Z	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Guo, X	en_US
dc.contributor.author	Sun, X	en_US
dc.date.available	2020-05-25T19:21:00Z
dc.date.issued	2016-11-01	en_US
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2016, 56 pp. 91 - 101	en_US
dc.identifier.issn	0952-1976	en_US
dc.identifier.uri	http://hdl.handle.net/10453/62191
dc.description.abstract	© 2016 Elsevier Ltd The multi-fault rush repair problem (MRRP) in power distribution networks is a discrete dynamic combinatorial problem with topology constraints and a series of uncertain factors in repairing process. This paper aims to obtain a robust repairing strategy by studying the uncertainty of fault repairing time in order to minimize the outage loss and total repairing time. To solve the above problem, sensitive faults will be considered to obtain the robust repairing. The robust repairing time model is proposed based on timed Petri Net model with inhibitor arcs, which is adopted to analyze the repairing process to obtain the impact factor of every fault in a power distribution network. To simulate the uncertainties of repairing process, a Latin hypercube sampling method combined with the simultaneous backward reduction algorithm is used to generate simulation scenarios. The continuous bacterial colony chemotaxis (BCC) optimization algorithm is revised to be applicable for integer variables so as to find the optimal solution of each scenario in MRRP. Then the improved minimax regret criterion is applied to decide the final optimal robust repairing strategy. This approach is verified by the standard IEEE 33-bus system and a real-world power distribution network. Scenarios with deterministic and uncertain repairing time are discussed and the simulation results show the robustness and effectiveness of the approach.	en_US
dc.relation.ispartof	Engineering Applications of Artificial Intelligence	en_US
dc.relation.isbasedon	10.1016/j.engappai.2016.08.020	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Method to decide a multi-fault rush repair robust strategy in power distribution networks	en_US
dc.type	Journal Article
utslib.citation.volume	56	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	090607 Power and Energy Systems Engineering (excl. Renewable Power)	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	56	en_US

Abstract:

© 2016 Elsevier Ltd The multi-fault rush repair problem (MRRP) in power distribution networks is a discrete dynamic combinatorial problem with topology constraints and a series of uncertain factors in repairing process. This paper aims to obtain a robust repairing strategy by studying the uncertainty of fault repairing time in order to minimize the outage loss and total repairing time. To solve the above problem, sensitive faults will be considered to obtain the robust repairing. The robust repairing time model is proposed based on timed Petri Net model with inhibitor arcs, which is adopted to analyze the repairing process to obtain the impact factor of every fault in a power distribution network. To simulate the uncertainties of repairing process, a Latin hypercube sampling method combined with the simultaneous backward reduction algorithm is used to generate simulation scenarios. The continuous bacterial colony chemotaxis (BCC) optimization algorithm is revised to be applicable for integer variables so as to find the optimal solution of each scenario in MRRP. Then the improved minimax regret criterion is applied to decide the final optimal robust repairing strategy. This approach is verified by the standard IEEE 33-bus system and a real-world power distribution network. Scenarios with deterministic and uncertain repairing time are discussed and the simulation results show the robustness and effectiveness of the approach.

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http://hdl.handle.net/10453/62191