Distribution network planning algorithm based on Hopfield neural network

1 Introduction An urban power system is a very important part othe power system and requires a huge investment for itconstruction and operation. This investment can bsubstantially reduced by a system approach to urbapower system planning which is not an easy task due tits dependence upon urban geography conditions. Foexample, feeder lines must be laid along urban streeUp to now, several mathematical models analgorithms have been developed to plan urban powesystem. Peponis and Papadopoulos [1]…

Distribution network planning algorithm based on Hopfield neural network

This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.