An ANN based network reconfiguration approach for voltage stability improvement of distribution network

Abstract

Recent trend in power sector is to automate distribution system to improve their reliability, efficiency and service quality. To facilitate automation of distribution system, an Artificial Neural Network (ANN) based novel methodology for enhancement of voltage stability by network reconfiguration is presented in this paper. Network reconfiguration is a process which alters the feeder topological structure by changing the open/close status of the sectionalizing (normally closed) and ties switches (normally open) in the system. A new voltage stability index is developed for voltage stability assessment of whole distribution network. In this work, a two stage search of switching option i.e. local search and global search is implemented to achieve desired network configuration. A multilayer ANN model with Error Back Propagation Learning (EBPL) algorithm is simulated for global search to obtained optimal set of candidate switching. The proposed scheme is tested on an 11 kV practical radial distribution system consisting of 52 buses. The experimental results are promising and encouraging. After reconfiguration, better voltage stable condition of the system is attained. Other objectives which are also satisfied are minimization of active and reactive power losses and improvement of voltage profile of most of the buses.