Distribution Systems

We develop systematic control and grid-forming control frameworks to enable stable, resilient, and intelligent operation of large-scale converter-dominated grids. Our work further advances AI-enabled protection and situational-awareness methods, addressing the challenges introduced by low short-circuit currents, heterogeneous devices, and increasingly digitalized infrastructures. In parallel, the team designs and prototypes grid-edge devices and intelligent operational technologies that support real-time monitoring, control, and protection in next-generation electric energy systems.

dis sys

Control

Systematic Grid-Forming Control Frameworks

Our work has advanced the fundamental understanding and system-level design of grid-forming control for IBRs. We have established unified and systematic control frameworks that enable transmission-level IBRs and distribution-level DERs to provide intelligent, independent, fast, and robust frequency and voltage regulation. These developments streamline the diverse operating modes required by IEEE 1547-2018 and IEEE P2800, and yield a coherent control architecture for stable, coordinated inverter operation under varying grid conditions. This line of research has led to a patent application and underpins major DOE-supported projects on power-electronics-dominated system operation.

 

Protection

Intelligent Protection in Distribution Systems

The team has developed advanced AI and machine-learning–based methods for modern power-system protection, with a particular focus on converter-dominated distribution networks where traditional overcurrent schemes become ineffective due to reduced and highly variable short-circuit currents. The team’s work introduces data-driven fault detection and classification approaches that remain effective under low-fault-current conditions, heterogeneous DER integration, and rapidly changing converter control dynamics. These methods leverage ML-based feature extraction, pattern recognition, and adaptive decision-making algorithms to improve the accuracy and robustness of fault identification in distribution feeders. This research provides important technical foundations for the development of next-generation intelligent protection strategies in power-electronics-dominated grids.

Impacts of DERs on protection of distribution systems
  • We performed a quantitative analysis of the short circuit fault characteristics of a distributed PV system and identified their impact on the traditional overcurrent protection of distribution systems. [C1]
Protection of distribution systems integrated with DERs
  • We proposed an intelligent current protection algorithm applying a machine learning algorithm, i.e., the radial bias function neural network (RBFNN). It can online detect the low fault currents that are fed from DERs and also difficult to detect by conventional relays in distribution grids. [C2]