Research

1. UIC PI, “Demonstrate a Community-Oriented Interoperable Control Framework Aggregating and Integrating DERs and Other Grid-edge Devices,” awarded by DOE in 2023.
2. Team Lead PI, “Enabling 100% Renewable Energy Integration: Creativity-based Co-design and Demonstration of Intelligent Modeling, Protection, and Grid-edge Control of Bulk Power Systems (RENEWS),” awarded by DOE in 2023.
3. Team Lead PI, “Enabling Ultra-Scale Offshore Wind Integration into Northeastern Power Grids,” under the Innovation in Offshore Wind Solicitation 2.0 of the National Offshore Wind Research and Development Consortium (NOWRDC), 2022 (Received the Selection Letter but not funded yet due to insufficient funding)
4. UIC PI, “Enabling Reliable Ultra-Scale Offshore Wind Energy Integration into New York Power Grids,” awarded by Ørsted and Eversource Energy in 2021.
5. Co-PI, “Real-Time-Simulation and Hardware-in-the-Loop (RTS-HIL) Scalable and Flexible Software/Hardware Platform for Emerging DOD R&D Needs,” awarded by DOD in 2020.
6. UIC PI, “Unified Universal Control and Coordination of Inverter-Based Resources, AI Forecasting, and Demonstration for PV+Battery Hybrid Plants,” awarded by DOE in 2020.

Next-generation power systems will be dominated by power electronics, spanning transmission, sub-transmission, distribution, and data-center-class architectures. Our research focuses on the modeling, control, protection, and security of power-electronics-dominated power systems and digitally integrated infrastructures. We work at the intersection of power systems, power electronics, high-voltage engineering, and emerging power architectures for AI and data-center infrastructures, with an emphasis on system resilience, stability, reliability, and intelligent operation to support large-scale converter-dominated grids and advanced data-center power environments.

Modeling

Dynamic modeling and stability analysis

PMU-based online modeling of HVDC for real-time stability detection of hybrid AC/DC systems

• We proposed an intelligent white-box modeling methodology to develop a new PMU based HVDC model. This model can be applied to online, fast, and accurately detect voltage stability of hybrid AC/DC systems by the Thevenin impedance matching. [J6, C6, R4]
• Other applications of the proposed model include online frequency stability detection, fault location, and load shedding determination.

EMTP modeling and transient analysis

• We proposed an electromagnetic model of the modular multilevel converter (MMC) based HVDC to analyze its energization transients. [C8, C9]
• We designed a circuit breaker (CB) with a pre-insertion resistor (PIR), which can be applied to suppress inrush currents during energization of the MMC-HVDC. [C9]
• We developed an innovative electromagnetic model of a complex CB based on Siemens PTI software and proposed a mathematical algorithm to estimate the amplitude of the direct-current (DC) component generated during energization of compensated lines. [C7]

The corresponding paper was selected by the technical program committee as the “Best Paper” (Top 5%) on power system modeling and simulation in the 2016 IEEE PES General Meeting (leading conference). Dr. He was featured in a Siemens News article to acclaim the “Best Paper” and praise Dr. He’s contribution to the very innovative modeling of a complex circuit breaker.

Control and Stability

VSC-based novel excitation system

• We invented paralleled multiple voltage/current source converter (VSC/CSC) based self-shunt excitation systems. The invented excitation systems can not only (1) achieve the function of the conventional excitation system, but are also able to (2) flexibly control the reactive power exchange of the ac side of the VSC/CSC with the generator terminal to provide additional damping. [P1, P2]
• We developed an optimal coordinating damping control strategy of the VSC based self-shunt excitation system applying the LQR method to maximize the damping capability of the invented VSC based excitation system to suppress low frequency inter-area oscillations. [J4, J2]

Voltage and frequency control of VSC-HVDC connected offshore wind farms

• We developed the droop-based coordinating control of the multi-terminal VSC-HVDC to integrate offshore wind generators to mainland transmission systems. [J5, R4]
• We also developed frequency control strategies to enable offshore wind farms to provide primary and secondary frequency regulation services to mainland transmission systems. [R3]

Wide-area measurement and control

• We proposed a direct measurement methodology to online identify the rotor angle of the synchronous generator. This methodology can be integrated into WAMSs to improve the reliability of rotor angle stability detection. [J3]
• In the existing rotor angle stability detection schemes, the phase angle of the terminal voltage of a generator is usually assumed to be its rotor angle since the generator rotor angle is hard to be measured accurately. The caused error by this assumption can be eliminated by the proposed methodology effectively.

Protection

Impacts of IBRs on protection of power systems

• We investigated the performance of doubly fed induction generators (DFIGs) during a short-circuit fault, and studied the impact of their low voltage ride through (LVRT) behavior on distance protection of AC grids. It is found that the fault on the terminal line of DFIG wind farms can induce mis-coordination of distance relays, leading to system security problems. [C5]
• We explored the performance of the VSC-HVDC during a short-circuit fault on its AC side, and researched the corresponding impact on AC distance relays. It is verified that the control of the VSC-HVDC can result in the miscoordination of protective relays. [C6]

Protection coordination of hybrid AC/DC grids

• We proposed an apparent impedance calculation algorithm, which utilizes the bus impedance matrix (Zbus) to calculate the impedances viewed by distance relays during a three-phase SC fault. [J5, R4]
• The proposed protection coordination algorithm can be applied to fast identify the protective device settings on an AC grid that need to be adjusted due to HVDC control of offshore wind generators.
• This algorithm can also determine the miscoordinated boundary and appropriate relay settings.

Equipment Operation and Maintenance

Detection of vegetation encroachment of overhead transmission lines

• We proposed a deep-learning based detection framework with advanced stereovision that utilizes the images obtained from vision sensors mounted on power transmission towers.
• The proposed framework provides an accurate, cost-effective, and practical vegetation detection methodology, which can provide early warning services for the vegetation management of utilities to detect vegetation encroachment of overhead power lines to avoid potential contacts. [C10, J8]

HVDC Technology and Renewable Energy Integration

My research has advanced the modeling, control, and stability foundations of HVDC-connected offshore and onshore renewable systems. During my time at Siemens Global Headquarters in Erlangen, Germany, I led technical studies for national and international transmission operators, including MMC-HVDC control design, modulation strategies, black-start capability, and large-scale offshore wind integration. I also contributed to TWENTIES, a €56.8M flagship offshore-wind project funded under the European Commission’s FP7 ENERGY Program, where I developed dynamic modeling and multi-terminal HVDC control strategies for offshore wind integration. These efforts bridged industrial practice with academic innovation and directly informed state-of-the-art HVDC and renewable integration technologies.

Together, these foundational contributions strengthen the analytical, control, and protection principles required for next-generation power-electronics-dominated, hybrid AC/DC electric power systems, and underpin multiple large-scale federally and industry-supported initiatives.

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.

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]