Our lab’s core product is an optimization-based toolbox focused on the diagnosis and mitigation of high-impact threats (e.g. blackouts) in power systems, enhancing resilience across all stages of grid disturbance. Its key enabler, the SparseAct algorithm, identifies dominant sources of failure and recommends fast-acting, effective responses at a minimal set of targeted locations. Our work also synthesizes advanced power system modeling with artificial intelligence (AI) to improve the speed and accuracy of threat detection, analysis, and response.
The Exploiting of Sparsity
Sparsity is the special structure in vectors or matrices that only a small fraction of non-zero values exist – carrying essential information; whereas most other entries are zero – ignorable without losing significant accuracy. Our research is organized around exploiting the sparsity structures in system, data, and threats.
Core research ongoing: SparseAct for blackout resiliency
1. Pinpointing dominant sources of blackout Given a single scenario of overloading or contingencies, power grid may result in system collapse (i.e., blackout). In this project, we investigate proactive defense using sparse optimization to pinpoint the dominant sources of blackout failure. Find out the basic idea here. And the sparse idea has been extended to distribution grid (see related works here, here, and here) 2. Suggesting fast-acting corrective actions (Ongoing) 3. AI Acceleration for SparseAct Our prior work developed a ML warm starter (here) offering 3x speed up in simulation of cyberattack-induced disturbances.
Sparse inspection for cyber-resilient system monitoring
1. Pinpointing and rejecting random bad data and topology errors In real time when facing bad measurement devices and wrong switch statuses, the resulting anomalous data and topology errors prevents us from knowing the correct system conditions like voltage profile, line outages, etc. In this project, we investigate robust state estimator that can pinpoint these errors and retain accurate voltage and topology information. Find out more details here, here, and here. 2. Time-series AI: sparse learning for dynamic time-series anomaly detection Given sensors placed on dynamic graphs, how to detect anomalies? Our method, DynWatch, finds out a sparse set of relevant historical data via sparse temporal weighting. Find out more details here. 3. Synergizing state estimation and AI for broad false data detection Find out some quick results in my thesis. More publication coming out soon.
More broadly exploiting sparsity
Sparse Weighting: Adapting Learning Models to Non-IID Data Power system data distributions shift due to topology changes and operating conditions. We develop sparse temporal reweighting schemes to adapt learning models to these non-IID conditions. Find an application in time-series anomaly detection here.
Sparse Probabilistic Graphical Models for Power Systems The power grid’s sparse connectivity can be exploited to build more interpretable and efficient learning models. Our work explores probabilistic graphical models augmented with neural networks to incorporate grid topology and domain knowledge. Find an application of ML-based simulation warm-starter here.