Protecting inverter-interfaced microgrids is challenging as conventional time-overcurrent protection becomes unusable due to the lack of fault current. There is a great need for novel protective relaying methods that enable the application of protection coordination on microgrids, thereby allowing for microgrids with larger areas and numbers of loads while not compromising reliable power delivery. Tools for modeling and analyzing such microgrids under fault conditions are necessary in order to help design such protective relaying and operate microgrids in a configuration that can be protected, though there is currently a lack of tools applicable to inverter-interfaced microgrids. This paper introduces the concept of applying an optimization problem formulation to the topic of inverter-interfaced microgrid fault modeling, and discusses how it can be employed both for simulating short-circuits and as a set of constraints for optimal microgrid operation to ensure protective device coordination.

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

Hidden failures present a noticeable impact to the reliability of the United States electrical grid. These hazards are responsible for protective device misoperations and can cause multiple-element contingencies across nearby components, greatly increasing the likelihood of cascading events. This paper provides an in-depth overview of the causes and risks of hidden failures and discusses methods for identifying critical locations where hidden failures could pose a risk of cascading failure, with the ultimate goal being to identify efficient mitigation methods that can prevent their occurrence in protective relays.

With the proliferation of distributed generation into distribution networks, the need to consider fault currents in the dispatch problem becomes increasingly relevant. This paper introduces a method for adding fault current constraints into optimal power flow in order to reduce fault currents while minimizing generation cost. The optimal power flow problem is formulated as a single optimization problem with sub-networks representing the faults of interest. Having a single optimization problem allows the decision variables to be coupled across the optimal power flow and the fault current studies without having to iterate over possible solutions. The proposed method is applicable to unbalanced distribution networks, including those with transformers that introduce phase-shifts.

There is significant uncertainty about the potential effects of a high-altitude electromagnetic pulse (HEMP) detonation on the bulk electric system. This study attempts to account for such uncertainty, in using Monte-Carlo methods to account for speculated range of effect of HEMP contingency. Through task parallelism and asynchronous processing techniques implemented throughout simulation, this study measure the effects of 700 large-scale HEMP simulations on a 7173 bus synthetic power grid. Analysis explores how contingency severity varies, depending on initial contingency parameters. Severity indices were captured throughout simulation to measure and quantify the cascading nature of an HEMP event. Further development of HEMP simulation modeling is explored as well, which could augment forecasts of potential contingency events as well.