A new artificial neural network model can handle static as well as dynamic features of a power system with a considerably high degree of accuracy, according to a team of scientists at Argonne behind this.
In the real sense, America’s power grid system is dynamic and large, which makes it challenging to manage. From a practical perspective, human operators have the required skill to maintain systems when conditions are static. However, in the event, conditions change abruptly, for example due to sudden faults, operators tend to falter. They lack understanding of how the system should best adapt to satisfy system security and safety requirements.
To address this, a research team at the U.S. Department of Energy, Argonne National Laboratory has developed a novel approach for operators to understand how to act. The approach enables operators to better control power systems employing artificial intelligence. And, could help operators control power systems more effectively. This could improve the resiliency of power grid, says a recent article published in IEEE Transactions on Power Systems.
New approach enables to handle Static and dynamic Issues
Employing the new approach, involving a sole decision-making model with improved accuracy, it enables operators to make decisions taking into account static as well dynamic features of a power system.
“The decision to switch on or switch off a generator and learn its power output is an example of static decision. On the other hand, electrical frequency, which is associated with the speed of a generator is an example of dynamic feature. This is because electrical frequency fluctuates over time in the event of a disruption or an operation,” stated the costa-author of the study. Whilst, if static and dynamic formulations are included in the same model, it is practically impossible to solve.