Microgrids and Resilience: A Review

Abstract

Supervised learning methods have been found to exhibit inductive biases favoring simpler features. When such features are spuriously correlated with the label, this can result in suboptimal performance on minority subgroups. Despite the growing popularity of methods which learn from unlabeled data, the extent to which these representations encode spurious features is unclear. In this work, we explore the impact of spurious features on Self-Supervised Learning (SSL) for visual representation learning. We first empirically show that commonly used augmentations in SSL can cause undesired invariances in the image space, and illustrate this with a simple example. We further show that classical approaches in combating spurious correlations, such as dataset re-sampling during SSL, do not consistently lead to invariant representations. Motivated by these findings, we propose LATETVG to remove spurious information from these representations during pretraining, by regularizing later layers of the encoder via pruning. We find that our method produces representations which outperform the baselines on several benchmarks, without the need for group or label information during SSL.

An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances

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Per-Phase Power Controller for Smooth Islanded Transitions in Three-Phase Three-Wire Systems

“…whose output P * 3φ modifies the total three-phase output power of the inverter, by shifting the droop characteristic (5).…”

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Distributed Optimization of Islanded Microgrids Integrating Multi-Type VSG Frequency Regulation and Integrated Economic Dispatch

“…Many studies have focused on microgrid islanding as well as energy scheduling for microgrids, and literature [3] improved distribution network resilience by advancing static islanding for single or multiple time sections. Literature [4] investigated the microgrid recovery steps and their control algorithms used after extreme disasters that lead to widespread power outages.…”