Using Phase Swapping to Solve Load Phase Balancing by ADSCHNN in LV Distribution Network

There are a large amount of advantages to make efficient load phase balancing, such as loss minimization, energy restoration, security, reliability and voltage balance. Optimal load phase balance is obtained by solving the load re-distribution problem as a combinatorial optimization problem. This enables the best switching option that gives a balanced load arrangement among the phases and minimizes power loss to be arrived at. In this paper, adding decaying self-feedback continuous neural network (ADSCHNN) is applied to realize phase swapping for load re-arrangement in the low voltage circuit of the distribution network. The network energy function of the ADSCHNN is constructed for objective function that defines the load phase balancing problem. The ADSCHNN is applied to solve the problem when load is represented in terms of current flow at the connection points, and when load is defined in terms of the real power. The results obtained using ADSCHNN are compared with those from a heuristic algorithm, and from fuzzy logic expert system. Simulations results on real practical data show that the ADSCHNN is very effective and outperforms other known algorithms in terms of the maximum difference of the phase currents or powers.

[1]  D. E. Bouchard,et al.  Applications of Hopfield neural networks to distribution feeder reconfiguration , 1993, [1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems.

[2]  Y. Ogita,et al.  A parallel tabu search based method for reconfigurations of distribution systems , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[3]  Ji Jwo-Hwu.  The refined strategy for substation main transformer and feeder load balancing , 1997 .

[4]  Tsai-Hsiang Chen,et al.  Optimal phase arrangement of distribution transformers connected to a primary feeder for system unbalance improvement and loss reduction using a genetic algorithm , 1999 .

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

[6]  Jae-Chul Kim,et al.  Application of simulated annealing and tabu search for loss minimization in distribution systems , 2004 .

[7]  Taher Niknam,et al.  An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration , 2009 .

[8]  D.V. Nicolae,et al.  Reconfiguration and Load Balancing in the LV and MV Distribution Networks for Optimal Performance , 2007, IEEE Transactions on Power Delivery.

[9]  Tsai-Hsiang Chen,et al.  Optimal phase arrangement of distribution transformers connected to a primary feeder for system unbalance improvement and loss reduction using a genetic algorithm , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[10]  Jin-O Kim,et al.  Optimal service restoration and reconfiguration of network using Genetic-Tabu algorithm , 2004 .

[11]  C. N. Lu,et al.  A new approach for feeder reconfiguration to minimize distribution system loss , 1993, Proceedings. Joint International Power Conference Athens Power Tech,.

[12]  G. B. Jasmon,et al.  A new approach of distribution system reconfiguration for loss minimization , 2000 .

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

[14]  Abhisek Ukil,et al.  Feeder Load Balancing using Fuzzy Logic and Combinatorial Optimization-based Implementation , 2008, ArXiv.

[15]  J. Martín,et al.  A new heuristic approach for distribution systems loss reduction , 2008 .

[16]  Liang Jin,et al.  A Review of: “Static and Dynamic Neural Networks” , 2005 .

[17]  Mo-Yuen Chow,et al.  Phase balancing using mixed-integer programming [distribution feeders] , 1998 .

[18]  Mo-Yuen Chow,et al.  Phase balancing using simulated annealing , 1999 .

[19]  Gevork B. Gharehpetian,et al.  Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators , 2008, Appl. Math. Comput..

[20]  Taher Niknam,et al.  A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration , 2010, Eng. Appl. Artif. Intell..

[21]  Ching-Tzong Su,et al.  Feeder reconfiguration and capacitor setting for loss reduction of distribution systems , 2001 .