Prediction of Available Transfer Capability with the Penetration of DG using ANN Techniques

The modern power system is subject to transformation from centralized to a distributed one because of the rapid advancements in Distributed Generation (DG) technology. At the same time, the increased demand for electricity is resulting in transmission network congestion, and the transmission lines are pushed to operate closer to their limit. This raises a need for power system operators to evaluate and enhance the Available Transfer Capability (ATC) of existing transmission lines to relieve the congestion in transmission networks and improve the power system reliability and security. Several methods, viz. Sensitivity factors-based methods and repeated power flow methods are used to estimate ATC by performing power flow studies. This paper demonstrates the development of Artificial Neural Networks (ANN) to predict the day ahead ATC of power system with the penetration of DGs by using its past performance.