A Spanning Tree-based Genetic Algorithm for Distribution Network Reconfiguration

This paper presents a spanning tree-based genetic algorithm (GA) for the reconfiguration of electrical distribution systems with the objective of minimizing active power losses. Due to low voltage levels at distribution systems, power losses are very high and sensitive to system configuration. There...

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Bibliographic Details
Published in:Conference record of the Industry Applications Conference pp. 1 - 6
Main Authors: Gautam, Mukesh, Bhusal, Narayan, Benidris, Mohammed, Louis, Sushil J.
Format: Conference Proceeding
Language:English
Published: IEEE 10.10.2020
Subjects:
Active filters
Distribution system
Filtering algorithms
genetic algorithm
Genetic algorithms
Load flow analysis
Low voltage
network reconfiguration
power loss
Sociology
spanning tree
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ISSN:2576-702X
Online Access:Get full text
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Summary:This paper presents a spanning tree-based genetic algorithm (GA) for the reconfiguration of electrical distribution systems with the objective of minimizing active power losses. Due to low voltage levels at distribution systems, power losses are very high and sensitive to system configuration. Therefore, optimal reconfiguration is an important factor in the operation of distribution systems to minimize active power losses. Smart and automated electric distribution systems should be able to reconfigure as a response to changes in load levels to minimize active power losses. The proposed method searches spanning trees of potential configurations and finds the optimal spanning tree using genetic algorithm in two steps. In the first step, all the invalid combinations of branches and tie-lines (e.g. combinations which are not supplying power to some of loads) generated by initial population of GA are filtered out with the help of spanning tree search algorithm. In this second step, power flow analyses are performed for only those combinations that form spanning trees and the optimal configuration is determined based on the amount of active power losses (optimal configuration is one which results minimum power losses). The proposed method is implemented on several systems including the well-known 33-node and 69-node systems. The results show that the proposed method is accurate and efficient in comparison with existing methods.
ISSN:2576-702X
DOI:10.1109/IAS44978.2020.9334819