Optimal coordination of directional over-current relays using Teaching Learning-Based Optimization (TLBO) algorithm

Relay Coordination in a big distribution system with multiple meshes and bidirectional power feed becomes very Complex for protection engineers. Manual and graph theory based approaches were applied successfully in small power system. In a big distribution system linear and non-linear programming based optimizing techniques are applied for relay coordination. Presently, artificial intelligence techniques are applied for optimal co-ordination of directional overcurrent relays. This paper discusses the application of Teaching Learning Based Optimization (TLBO) algorithm for optimal coordination of DOCR relays in a looped power system. Combination of primary and backup relay is chosen by using Far vector of LINKNET structure, to avoid mis-coordination of relays. Coordination of DOCR is tested for IEEE 3, 4 and 6 bus systems using the TLBO. Also, the objective function is modified to optimize the operating time between backup and primary relays. The results are compared with the optimized values of Time dial setting and Plug setting values obtained from modified differential evolution algorithm. The proposed algorithm TLBO gives optimal coordination margin between 0.3 and 0.8 s and no miscoordination between primary and backup pairs. Results are also verified using Digsilient power factory simulation software.

► Optimal relay coordination of directional overcurrent relays in a meshed system.
► Teaching Learning Based Optimization algorithm is used to find the optimal setting.
► The minimum and maximum relay coordination margin between the primary and back up relays have been taken.
► Results are compared with earlier reported results.
► The result also tested with the system developed in Digsilient power factory.