A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model

An accurate and up-to-date grid topology is critical for situational awareness; however, switch statuses can often be inaccurate due to physical damage, communication error, or cyber-attack. This paper develops a circuit-theoretic node-breaker (NB) model to formulate a generalized state estimation (GSE) method. This ckt-GSE is convex, scalable, and robust to topology and measurement errors. The method first constructs an equivalent circuit representation of the AC power grid by developing and aggregating linear circuit models of continuous measurements (RTUs and PMUs) and the switching devices. Then based on a weighted least absolute value (WLAV) objective, the proposed ckt-GSE formulates a robust estimator as a Linear Programming (LP) problem whose solution includes a sparse vector of noise terms that separately localize wrong switch statuses and bad continuous measurements. This paper is the first to explore a circuit-theoretic approach for an AC-network constrained GSE algorithm that is: 1) applicable to the real-world setting of measurement devices, including both SCADA meters and phasor measurement units (PMUs); 2) convex without relaxation and, therefore, scalable and guaranteed to converge; and 3) intrinsically robust with the ability to detect, localize and reject different data errors

Ordinal depth from SFM and its application in robust scene recognition

Ph.DDOCTOR OF PHILOSOPH

Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning

Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high risk to the practicality of the methods. Specifically, ignoring the grid-specific spatiotemporal patterns (in load, generation, and topology, etc.) can lead to outputting infeasible, unrealizable, or completely meaningless predictions on new inputs. To address this concern, this paper investigates real-world operational data to provide insights into power grid behavioral patterns, including the time-varying topology, load, and generation, as well as the spatial differences (in peak hours, diverse styles) between individual loads and generations. Then based on these observations, we evaluate the generalization risks in some existing ML works causedby ignoring these grid-specific patterns in model design and training

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions