Artificial immune networks Copt-aiNet and Opt-aiNet applied to the reconfiguration problem of radial electrical distribution systems

Abstract This paper presents two new approaches to solving the reconfiguration problem of electrical distribution systems (EDS) using the Copt-aiNet (Artificial Immune Network for Combinatorial Optimization) and Opt-aiNet (Artificial Immune Network for Optimization) algorithms. The Copt-aiNet and Opt-aiNet algorithms are efficient optimization techniques inspired by the immune network theory (aiNet). The reconfiguration problem is a complex combinatorial problem that aims at identifying the best radial topology for the EDS in order to minimize power losses. A specialized forward/backward radial power flow was used to evaluate each proposed solution proposal in order to determine its power losses and its feasibility regarding the operational constraints of the EDS. The algorithms were developed in the C++ programming language and test systems of 33, 70, 84, 119, and 136 nodes, along with a real system of 417 nodes, were used to validate the proposed method. The obtained results were compared with the best solutions found in the specialized literature in order to verify the efficiency of the proposed algorithms.

[1]  M.A.L. Badr,et al.  Distribution system reconfiguration using a modified Tabu Search algorithm , 2010 .

[2]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[3]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[4]  Hsiao-Dong Chiang,et al.  Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results , 1990 .

[5]  M.A.L. Badr,et al.  Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm , 2009 .

[6]  Chun Wang,et al.  Optimization of Network Configuration in Large Distribution Systems Using Plant Growth Simulation Algorithm , 2008, IEEE Transactions on Power Systems.

[7]  Fernando José Von Zuben,et al.  An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data , 2004, Genetic Programming and Evolvable Machines.

[8]  R. Romero,et al.  An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem , 2008, IEEE Transactions on Power Systems.

[9]  Enrico Carpaneto,et al.  Distribution system minimum loss reconfiguration in the Hyper-Cube Ant Colony Optimization framework , 2008 .

[10]  C. Su,et al.  Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems , 2005 .

[11]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .

[12]  Herbert Schildt,et al.  Borland C++ Builder: The Complete Reference , 2001 .

[13]  Zhengcai Fu,et al.  An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems , 2007 .

[14]  M. Rider,et al.  Imposing Radiality Constraints in Distribution System Optimization Problems , 2012 .

[15]  D. Shirmohammadi,et al.  A compensation-based power flow method for weakly meshed distribution and transmission networks , 1988 .

[16]  Carlos A. Castro,et al.  Distribution systems operation optimisation through reconfiguration and capacitor allocation by a dedicated genetic algorithm , 2010 .

[17]  M. J. Rider,et al.  A mixed-integer LP model for the reconfiguration of radial electric distribution systems considering distributed generation , 2013 .

[18]  E. Lopez,et al.  Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application , 2006, IEEE Transactions on Power Systems.

[19]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[20]  Hoyong Kim,et al.  Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems , 1993 .

[21]  J. J. Grainger,et al.  Distribution feeder reconfiguration for loss reduction , 1988 .

[22]  Hong-Chan Chang,et al.  Network reconfiguration in distribution systems using simulated annealing , 1994 .

[23]  H. Salazar,et al.  Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems , 2006, IEEE Transactions on Power Delivery.

[24]  Jonathan Timmis,et al.  Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation , 2003, GECCO.

[25]  Fabrício Olivetti de França,et al.  An artificial immune network for multimodal function optimization on dynamic environments , 2005, GECCO.