75 citations · 144 across the 13 of their papers we have counts for
15 papers
Structured Differentiable Optimization for Efficient Decision-focused Learning in Power Systems
Wangkun Xu, Fei Teng
Decision-focused learning (DfL) trains forecasting models to align downstream decision consequences, such as power-system operating costs. However, its application to realistic pow…
Input Convex Neural Network as a Surrogate in Stability-Constrained Optimization for IBR-dominated Power Systems
Wangkun Xu, Hongyang Jia, Yi Wang +2
Input convex neural networks (ICNNs) are increasingly used as surrogates for stability indices and embedded as constraints in power-system optimization. This letter clarifies two r…
Flow-based Polynomial Chaos Expansion for Uncertainty Quantification in Power System Dynamic Simulation
Le Fang, Wangkun Xu, Fei Teng
The large-scale integration of renewable energy sources introduces significant operational uncertainty into power systems. Although Polynomial Chaos Expansion (PCE) provides an eff…
Learning-Augmented Power System Operations: A Unified Optimization View
Wangkun Xu, Zhongda Chu, Fei Teng
With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic…
Qubit-Efficient Quantum Annealing for Stochastic Unit Commitment
Wei Hong, Wangkun Xu, Fei Teng
Stochastic Unit Commitment (SUC) has been proposed to manage the uncertainties driven by renewable integration, but it leads to significant computational complexity. When accelerat…
On the Incorporation of Stability Constraints into Sequential Operational Scheduling
Wangkun Xu, Zhongda Chu, Florin Capitanescu +1
With the increasing penetration of Inverter-Based Resources (IBRs), power system stability constraints must be incorporated into the operational framework, transforming it into sta…
Efficient Sampling for Data-Driven Frequency Stability Constraint via Forward-Mode Automatic Differentiation
Wangkun Xu, Qian Chen, Pudong Ge +2
Encoding frequency stability constraints in the operation problem is challenging due to its complex dynamics. Recently, data-driven approaches have been proposed to learn the stabi…
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning
Wangkun Xu, Jianhong Wang, Fei Teng
Successful machine learning involves a complete pipeline of data, model, and downstream applications. Instead of treating them separately, there has been a prominent increase of at…
Task-Aware Machine Unlearning and Its Application in Load Forecasting
Wangkun Xu, Fei Teng
Data privacy and security have become a non-negligible factor in load forecasting. Previous researches mainly focus on training stage enhancement. However, once the model is traine…
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
Wangkun Xu, Fei Teng
The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However,…
Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach
Wangkun Xu, Martin Higgins, Jianhong Wang +2
Fast and accurate detection of cyberattacks is a key element for a cyber-resilient power system. Recently, data-driven detectors and physics-based Moving Target Defences (MTD) have…
Cyber-Physical Risk Assessment for False Data Injection Attacks Considering Moving Target Defences
Martin Higgins, Wangkun Xu, Fei Teng +1
In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Many works…
Robust Moving Target Defence Against False Data Injection Attacks in Power Grids
Wangkun Xu, Imad M. Jaimoukha, Fei Teng
Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flex…
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
Jianhong Wang, Wangkun Xu, Yunjie Gu +2
This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL). The e…
A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System
Wangkun Xu, Fei Teng
As one of the largest and most complex systems on earth, power grid (PG) operation and control have stepped forward as a compound analysis on both physical and cyber layers which m…