系统与控制
The rapid growth of artificial intelligence workloads is increasing the scale and concentration of data center demand, creating new concerns for power system resilience under disruptive events. This paper extends a validated multi-time-step…
Sampling-based model predictive control methods handle nonlinear dynamics and complex cost landscapes through Monte Carlo rollouts, yet typically employ fixed constraint penalties that do not adapt to model-plant mismatch. This paper…
Model Predictive Path Integral (MPPI) control is directly implementable on nonlinear systems because its online update requires only forward rollouts of the dynamics, not gradients, linearizations, or convex optimization. However, this…
Variable-speed pumped storage hydropower (VS-PSH) must honor short-block dispatch commitments while limiting the operational degradation that intensified regulation duty inflicts on its components. When a single controller pursues both aims…
Purpose: To compare detuning performance and evaluate the power requirements of optical detuning methods, and to demonstrate the feasibility of an optically detuned four-channel receive array. Methods: Four optical detuning methods were…
Satellite state estimation plays a fundamental role in orbital navigation, tracking, and autonomous space operations. Accurate estimation remains challenging due to uncertainties in process and measurement noise, which may degrade the…
The increasing penetration of inverter-based resources (IBRs) into bulk power systems has fundamentally altered short-term voltage dynamics following disturbances. Conventional short-circuit capacity (SCC) metrics provide a useful screening…
Lane-change, a triggering of traffic disturbances to the upstream vehicles, is detrimental to traffic safety and efficiency. Coupled with car-following behavior, the joint maneuvers depict the general picture of how traffic disturbances…
This paper proposes Variational Inference-based Bayesian Estimation with Sobol screening (VIBES), a two-stage scalable framework for Bayesian uncertainty quantification (UQ). The proposed approach combines Sobol global sensitivity analysis…
Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of realistic, openly available datasets remains a major barrier for benchmarking and…
This paper studies input-to-state stability (ISS) certification for data-driven Koopman learning control of unknown discrete-time nonlinear repetitive systems over finite trial horizons. Rather than proposing a new learning law, we certify…
Adaptive control learns the plant online; neural-operator control learns the control gains offline. We bring the two together for a class of nonlinear hyperbolic PDEs whose dynamics are governed by an unknown Volterra series of arbitrarily…
This paper systematically analyzes the relationships among the $dq$-domain, $\alpha\beta$-domain, and sequence-domain representations used in small-signal impedance modeling of voltage-source converters (VSCs). It is shown that the AC…
Virtual admittance (VA) is widely used in cascaded voltage-control and current-control (VC-CC) grid-forming inverters (GFMIs) because it shapes the converter terminal behavior while preserving the current-regulation path required for…
Industrial prediction and soft sensing depend on credible input measurements. In field deployment, a predictor may receive biased, delayed, stale, or derived measurements that still look plausible. Prediction can then fail before the…
Efficient model order reduction for many-port resistor-capacitor (RC) networks is essential in post-layout circuit simulation. Existing high-accuracy elimination-based methods have certain limitations, such as fixed frequency points, large…
The concept of Digital Twin (DT) consists of a physical asset, a digital asset, and their bidirectional data exchange, differing the DT from concepts with lower level of integration. Availability of the bidirectional interconnection not…
Black-box modeling of inverter-based resources (IBRs) has become essential for real-time grid operation and control in the presence of proprietary electronic control architectures. Existing machine learning (ML)-based online dynamic…
Converters-based systems like wind farms manifest themselves as control-intensive systems, where control-driven stability issues frequently occur, e.g., oscillations. Such issues are popularly studied via circuit impedance-based methods.…
Power systems consist of dynamically coupled generators, motivating the use of Graph Neural Networks (GNNs) for online transient stability prediction. Traditional GNN frameworks are often constrained by fixed admittance-based topologies…