Fast distribution network reconfiguration with graph theory. Issue 13 (22nd May 2018)

• Record Type: Journal Article
• Authors: Huang, Shengjun; Dinavahi, Venkata
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 13(2018)
• Page Start: 3286
• Page End: 3295
• Publication Date: 2018-05-22
• Subjects: distribution networks -- graph theory -- particle swarm optimisation -- integer programming -- probability -- decoding -- load flow -- matrix algebra; fast DN reconfiguration -- graph theory -- nonlinear properties -- mixed‐integer properties -- distribution network reconfiguration problem -- DNRC problem -- meta‐heuristic algorithms -- solution representation -- fitness evaluation -- integer numbers -- binary numbers -- probability‐based loop destruction strategy -- decimal encoding -- power flow calculation -- radial network -- backward‐forward sweep method -- direct solution technique -- matrix generation process -- benchmark systems -- particle swarm optimisation -- DNRC; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.0228
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Owing to mixed‐integer and non‐linear properties, the distribution network reconfiguration (DNRC) problem has been widely addressed with meta‐heuristic algorithms. To accelerate the solution process, two essential components of meta‐heuristic algorithms are investigated in this study: solution representation and fitness evaluation. Instead of the popular binary and integer numbers, decimal encoding is employed. Decoding is based on the proposed probability‐based loop destruction strategy. The fitness evaluation is based on the power flow calculation of radial network. Different from backward/forward sweep method, the advantageous direct solution technique is utilised, where the matrix generation process has been accelerated. Both improvements are based on the graph theory and fully explained with illustrative examples. Case studies are implemented on five benchmark systems. The superiority of the proposed methods over their advanced counterparts has been established with intensive comparisons. Finally, these methods are integrated into a standard particle swarm optimisation framework for the solution of DNRC. Results indicate that the proposals significantly improve the solution efficiency without the loss of quality.