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Found 14 papers, 1 papers with code
VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence
We require (a) convergence to a global empirical loss minimizer when adversaries are absent, and (b) either detection of adversarial presence of convergence to an admissible consensus irrespective of the adversarial configuration.
Model Predictive Control for Joint Ramping and Regulation-Type Service from Distributed Energy Resource Aggregations
Distributed energy resources (DERs) such as grid-responsive loads and batteries can be harnessed to provide ramping and regulation services across the grid.
An Adversarial Approach to Evaluating the Robustness of Event Identification Models
Thorough experiments on the synthetic South Carolina 500-bus system highlight that a relatively simpler model such as logistic regression is more susceptible to adversarial attacks than gradient boosting.
A Semi-Supervised Approach for Power System Event Identification
Using this package, we generate and evaluate eventful PMU data for the South Carolina synthetic network.
Robust Model Selection of Gaussian Graphical Models
We first characterize the equivalence class up to which general graphs can be recovered in the presence of noise.
The Saddle-Point Accountant for Differential Privacy
SPA approximates privacy guarantees for the composition of DP mechanisms in an accurate and fast manner.
Parameter Estimation in Ill-conditioned Low-inertia Power Systems
This ill-conditioning is because of converter-interfaced power systems generators' zero or small inertia contribution.
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime
Since the optimization problem is infinite dimensional, it cannot be solved directly; nevertheless, we quantize the problem to derive near-optimal additive mechanisms that we call "cactus mechanisms" due to their shape.
A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets.
Generation of Synthetic Multi-Resolution Time Series Load Data
The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available.
Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks
A framework for the generation of synthetic time-series transmission-level load data is presented.
A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid
In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS).
Three Variants of Differential Privacy: Lossless Conversion and Applications
In the first part, we develop a machinery for optimally relating approximate DP to RDP based on the joint range of two $f$-divergences that underlie the approximate DP and RDP.
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of R\'enyi differential privacy (RDP).
