Optimal Service Restoration Scheme for Radial Distribution Network Using Teaching Learning Based Optimization

In the event of a fault isolation process, all loads located downstream from faulted point become out service, and as consequence, power interruption affects greater portion radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing network topology configuration via tie-switch section switch combinations. However, when is performed, it results in increased load currents. As result, some Protective Devices (PDs) can operate undesirably branches may unprotected. Therefore, essential to consider protection constraints SR problem maintain service continuity during interruptions. The proposed aims at with minimum out-of-service loads, loss, improved voltage profiles same time ensures PDs correctly normal overloading conditions. was carried on Debre Markos networks, using Teaching Learning Based Optimization (TLBO), Particle Swarm (PSO), Differential Evolutionary (DEV) algorithms. considering without constraints. obtained not feasible for constraints, since fail properly loading After executing algorithms by single case, loss reductions TLBO, DEV, PSO were 64.9073%, 45.9073%, 55.358 %, respectively. each algorithm 0.96%, 0.95%, algorithm, except branch under fault, healthy restored. When considered SR, current did exceed rating fuses. show importance prevent dysfunction network. Comparative analyses TLBO performed better than DEV search functions.