A5042967962- Power System Optimization and Stability
- Quantum Computing Algorithms and Architecture
- Microgrid Control and Optimization
- Quantum Information and Cryptography
- Optimal Power Flow Distribution
- Power Systems and Renewable Energy
- Smart Grid and Power Systems
- Smart Grid Energy Management
- High-Voltage Power Transmission Systems
- Quantum-Dot Cellular Automata
- Magnetic confinement fusion research
- Islanding Detection in Power Systems
- Advanced Memory and Neural Computing
- Computability, Logic, AI Algorithms
- Power System Reliability and Maintenance
- Advancements in Semiconductor Devices and Circuit Design
- Energy Load and Power Forecasting
- Low-power high-performance VLSI design
- Model Reduction and Neural Networks
- Fluid Dynamics and Vibration Analysis
- Wind Turbine Control Systems
- Smart Grid Security and Resilience
- Power Systems and Technologies
Pennsylvania State University
2022-2025
Central South University of Forestry and Technology
2025
Central South University
2025
Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed promote energy sustainability. In those systems, knowing the maximum sections of and data delivery essential monitoring, operation, control. However, high effort required. By leveraging quantum resources, Approximate Optimization Algorithm (QAOA) means...
Abstract Convolutional neural network (CNN), as a prominent machine learning model, is crucial in image classification and feature extraction by completely utilizing the correlation information of data. Currently, CNN facing challenges slow computing speed, expanding data scale poor interpretation for rapidly increasing Hilbert space. Fortunately, quantum convolutional (QCNN) promises an elegant solution to improve efficiency processing combining superiority capability CNN. However, most...
Distributed energy resources (DERs) offer a promising solution for enhancing the resilience and efficiency of modern power systems. However, inherent time delays in their communication control systems can degrade small signal stability, affecting reliable operation. This paper presents an Adaptive Step Size Linear Multistep Method (AS-LMS), analyzing stability system presence delays. The AS-LMS automatically adjusts step size based on computed behavior, achieving balance between accuracy...
Transient stability is crucial to the reliable operation of power systems. Existing theories rely on simplified electromechanical models, substituting detailed electromagnetic dynamics inductor and capacitor with their impedance representations. However, this simplification inadequate for growing penetration fast-switching electronic devices. Attempts extend existing include lead overly conservative conditions. To tackle problem more directly, we study condition under which source...
Modularized sparse identification (M-SINDy) is developed in this paper for effective data-driven modeling of the nonlinear transient dynamics microgrid systems. The high penetration power-electronic interfaces makes microgrids highly susceptible to disturbances, causing severe transients, especially islanded mode. M-SINDy method realizes distributed discovery by partitioning a higher-order system into multiple subsystems and introducing pseudo-states represent impact neighboring subsystems....
Modularized Koopman bilinear form (M-KBF) is presented to model and predict the transient dynamics of microgrids in presence disturbances. As a scalable data-driven approach, M-KBF divides identification prediction high-dimensional nonlinear system into individual study subsystems, thus, alleviates difficulty intensively handling high volume data overcomes curse dimensionality. For each subsystem, established efficiently identify its by identifying isotypic eigenfunctions via Extended...
Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems, especially for Distributed Energy Resources (DERs) dominant cyber-physical systems that have been widely developed promote energy sustainability. The systems’ maximum or data sections are essential monitoring, operation, and control, while high effort is required. Approximate Optimization Algorithm (QAOA) promising means search these by leveraging quantum resources....
The evolution of quantum computers has encouraged research into how to handle tasks with significant computation demands in the past few years. Due unique advantages parallelism and entanglement, various types machine learning (QML) methods, especially variational classifiers (VQCs), have attracted attention many researchers been developed evaluated numerous scenarios. Nevertheless, most on VQCs is still its early stages. For instance, as a consequence mathematical constraints imposed by...
A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in presence disturbances. The graph-based architecture PIDGeuN provides a natural representation microgrid topology. Using only state information that practically measurable, employs time delay embedding formulation fully reproduce system dynamics, avoiding dependency conventional methods on internal dynamic states, e.g.,...
With the increasing number of ongoing replacement conventional power plants by distributed energy resources (DERs), system becomes more vulnerable to operational disturbance. It is necessary accurately estimate inertia DERs-dominant systems, based on which can be tuned and stabilized after disturbances. A disturbance-induced estimation method introduced in this paper for identifying microgrid systems with power-electronics-interfaced DERs. This calculates total change response a disturbance...