Neural networks in RSCAD for intelligent real-time power system applications

Addition of load and generation over the existing transmission lines over the past several years has added to the complexity of the electric power network. Smart grid will have high uncertainty and variability owing to high penetration of intermittent energy sources such as wind and solar. At the same time, higher efficiencies and reliability is demanded and the electrical power network is being pushed closer to its operating limits. New equipments and technologies are being developed in order to cope with these requirements. It is necessary to have real-time simulation of power system phenomena, components, and intelligent methods of monitoring, communication and control under these changed scenarios in order to ensure stability, reliability, integrity and security of the electric power grid. The RTDS^®, along with its graphical interface RSCAD, provides the ability to design and simulate power systems and controls, and perform hardware-in-the-loop studies. In this paper, neural networks (NN) library developed for RSCAD is applied to intelligent power system applications. The results show that the NN library developed in RSCAD can be an important tool in the study and development of intelligent methods for smart grid applications.