Abstract
This paper presents novel cycle-break (spanning tree generation) algorithms which can be used to find the optimal distribution network topology. These algorithms (adjacency matrix/top-down/bottom-up cycle break) represent a novel way of obtaining radial network topology by cycle regrouping using adjacency matrix or elementary cycle information. Proposed methods assure connected radial network topology and can be used in combination with genetic algorithms to obtain optimal distribution network structure under minimum active power loss or network loading index framework. The cycle-break algorithms are used in initial population generation, crossover and mutation process to enhance the performance of the genetic algorithms in terms of convergence rate. These modifications make the proposed approach suitable for the use on realistic distribution networks without concern of its complexity. The algorithms are tested on a several standard test networks and the results are compared with the other existing approaches.
Highlights
This paper presents new approach for optimal distribution network (DN) reconfiguration using the combination of novel cycle break algorithms (based on adjacency matrix (AM) or elementary cycles (EC):‘bottom-up’ (BU) and ‘top-down’ (TD) approach) and genetic algorithms (GA)
In order to enhance the efficiency of the crossover process, two main goals need to be achieved: first it is necessary to assure that the generated offspring has radial network structure without the need for topology rechecking or correction, and second, good genetic material needs to be transferred to offspring to enhance the convergence rate
This paper presents novel cycle break algorithms which use network AM or EC information to produce radial network structures
Summary
This paper presents new approach for optimal distribution network (DN) reconfiguration using the combination of novel cycle break algorithms (based on adjacency matrix (AM) or elementary cycles (EC):‘bottom-up’ (BU) and ‘top-down’ (TD) approach) and genetic algorithms (GA). There have are many approaches that employ GA for optimal DN reconfiguration, but most of them have issues with assuring the DN radial topology, have long computational time or suffer implementation difficulties on real-world distribution networks [4,5,6,8,9,10,11] By analyzing all these approaches and taking into consideration all the DN constraints, proposed cycle-break algorithms based on AM and EC can be recognized as the algorithms that surpasses these problems and do not violate DN radiality and connectivity constraints. The cycle-break algorithm which uses the AM assures radial topology based on information from initial adjacency matrix which is modified each time the network branch is switched off. These topology changes, in relation to the initial meshed network topology, are accounted for through the AM and EC information modifications, using the procedures which are described later on
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