Integration of Artificial Intelligence for Enhanced Coordination of DOCR Protection in Distributed Generation Systems

Abstract

Distributed generation (DG) is an approach that involves adding decentralized power generation within a distribution network. Distributed generation systems can reduce transmission losses, increase the reliability of energy supply, minimize carbon emissions, and enable the active participation of consumers in energy production. However, with the increase in distributed generation, electric power systems face new challenges in maintaining operational reliability and safety. Disruptions such as short circuits or overcurrent can occur in the system, and appropriate protective responses are required to protect the power grid from more significant damage. The addition of DG also causes the short circuit current to vary and results in system protection coordination having to be redone. Carrying out coordination will take a long time. This research uses modeling and simulation of a distributed generation system with various operating conditions and works adaptively according to changes in the system due to the addition of DG. The results obtained from the simulation are used in neural network training to study the relationship patterns between directional overcurrent relays (DOCR) parameters and system operating conditions. The backpropagation algorithm is used in the Artificial Neural Network (ANN) training process. The training process utilizes the maximum Short Circuit Current (ISC) input obtained through generation, fault location, and fault type. Time Dial Setting (TDS) and Ipickup values are used as ANN training targets. After testing, the results obtained are in accordance with the target data. The efficacy of this method is further demonstrated through ETAP simulations, which confirm that ANN is a suitable approach for modeling adaptive and optimal relay coordination systems.