Intelligent Line Congestion Prognosis in Active Distribution System Using Artificial Neural Network

This paper proposes an intelligent line congestion prognosis scheme based on wide-area measurements, which accurately identifies an impending congestion and the problem causing the congestion. Due to the increasing penetration of renewable energy resources and uncertainty of load/generation patterns in the Active Distribution Networks (ADNs), power line congestion is one of the issues that could happen during peak load conditions or high-power injection by renewable energy resources. Congestion would have devastating effects on both the economical and technical operation of the grid. Hence, it is crucial to accurately predict congestions to alleviate the problem in-time and command proper control actions; such as, power redispatch, incorporating ancillary services and energy storage systems, and load curtailment. We use neural network methods in this work due to their outstanding performance in predicting the nonlinear behavior of the power system. Bayesian Regularization, along with Levenberg-Marquardt algorithm, is used to train the proposed neural networks to predict an impending congestion and its cause. The proposed method is validated using the IEEE 13-bus test system. Utilizing the proposed method, extreme control actions (i.e., protection actions and load curtailment) can be avoided. This method will improve the distribution grid resiliency and ensure the continuous supply of power to the loads.