Dynamic Self‐Healing System for Distribution Networks in Smart Grid

The goal of this project is to develop and implement a novel, dynamic, and real-time self-healing multilevel control concept for the existing grids as well as for the future SG. This is achieved by using machine learning technologies. The suggested solution is based on a machine learning approach for improving the online predictability and controllability of the smart grids. The solution aims to enhance the power system performance by managing the supply and demand sides reliably and efficiently during normal and contingencies conditions. The objective is to develop advanced procedures, proper strategies, and practical techniques for continuously monitoring, analyzing, and optimizing the power grid through self-healing multilevel control with uncertainty in the smart grid. This will help to improve the performance of electric networks, to offer a more efficient, safe, and reliable service, and to increase the consumer’s satisfaction.
The proposed real-time dynamic self-healing strategies will manage the smart grid performance during grid-connected and islanded modes of operation. The proposed system will be able to consider many factors such as: uncertainties of grid faults, load fluctuations, sophisticated data collection, ubiquitous correlations among diverse data sources, the size and complexity of grid datasets, heterogeneous structure, fast data architectures, grid integration challenges, market participants, control structure, intrinsic characteristics of the faulty signals, optimization of the fault-tolerant system, and performance degradation. To validate the developed methodologies, a comprehensive dynamic simulation model of a complex interdependent large-scale smart distribution network will be considered. The developed self-healing system, which uses machine learning, will be tested in a power grid in Qatar. More specifically, Lusail’s city grid will be simulated and considered as a case study. The research activities and results will be coordinated with Qatar’s utility company, KAHRAMA.