Unprecedented winter storms that hit across Texas in February 2021 have caused at least 69 deaths and 4.5 million customer interruptions due to the wide-ranging generation capacity outage record-breaking electricity demand. While much remains be investigated on what, how, why such wide-spread power outages occurred Texas, it is imperative for broader macro energy community develop insights policy making based a coherent electric grid model data set. In this paper, we collaboratively release...

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with U.S. becoming epicenter of COVID-19 cases since late March. As begins to gradually resume economic activity, it is imperative for policymakers and power system operators take a scientific approach understanding predicting impact on electricity sector. Here, we release first-of-its-kind cross-domain open-access data hub, integrating from across all existing wholesale markets case, weather, cellular...

Abstract The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, reliable operation becomes increasingly challenging. In this paper, present PSML, first-of-its-kind open-access multi-scale time-series dataset, to aid in development data-driven machine learning (ML)-based approaches future grids. dataset synthesized from joint transmission and distribution...

Blockchain technologies are considered one of the most disruptive innovations last decade, enabling secure decentralized trust-building. However, in recent years, with rapid increase energy consumption blockchain-based computations for cryptocurrency mining, there have been growing concerns about their sustainable operation electric grids. This paper investigates tri-factor impact such large loads on carbon footprint, grid reliability, and electricity market price Texas grid. We release...

Power system operations data are sometimes limited in a given space due to collinearity. As such, the recorded around an operating point of concern may be deficient or isotropically dispersed. Consequently, online sensitivity identification using ordinary regression methods is prone large errors. In this paper, locally weighted ridge method proposed overcome problem. The norm-2 Tikhonov-Phillips regularization integrated into linear regression. algorithm then has ability keep stable if...

The increasing amount of data recorded during power system operations and recently developed data-driven methods make online sensitivity identification (SI) a possibility. However, due to the inherent properties systems - nonlinearity, time variance, collinearity effective that carry information are insufficient. Consequently, SI collected with existing may result in unexpected estimates. In this paper, sufficient condition guarantees success is proposed. their impacts on then investigated....

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with U.S. becoming epicenter of COVID-19 cases since late March. As begins to gradually resume economic activity, it is imperative for policymakers and power system operators take a scientific approach understanding predicting impact on electricity sector. Here, we release first-of-its-kind cross-domain open-access data hub, integrating from across all existing wholesale markets case, weather, cellular...

A two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this article. This leverages generative adversarial networks (GAN) generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize synthetically create massive eventful PMU data, which would otherwise be difficult obtain from the real world due critical energy infrastructure information (CEII) protection. To...

This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes. Due to confidentiality real PMU no public access power systems infrastructure information, lack credible realistic becomes a growing concern. Instead constructing grids then producing by time simulations, we propose model-free approach directly generate data. train generative adversarial network (GAN) data, which can be used capturing system dynamic behaviors. To...

This article presents a use-inspired perspective of the opportunities and challenges in massively digitized power grid. It argues that intricate interplay data availability, computing capability, artificial intelligence (AI) algorithm development are three key factors driving adoption solutions The impact these on critical functions system operation planning practices is reviewed illustrated with industrial practice case studies. Open research for data, computing, AI algorithms articulated...

Measurement sensors installed in the smart transmission system can acquire big data for electromechanical dynamics monitoring. The time-series obtained carry information of instantaneous relationship oscillation modes with respect to operating conditions. To extract this information, paper proposes a parallel processed online supervised learning algorithm called k-nearest neighbors "locally weighted linear regression" (KNN-LWLR), which is an extensive combination two famous machine-learning...

With deeper penetration of distributed energy resources (DERs) in the distribution system, power flow patterns are becoming more diverse, rendering over-current relays systems with potential malfunctioning. This paper proposes a data-driven approach to design operating strategies high (DERs). A support vector machine-based classifier for relay setting is introduced. Compared conventional relays, proposed method could operate accurately smaller false alarm rate. It also robust altering system...

This paper envisions a new control architecture for the protective relay setting in future power distribution systems. With deepening penetration of distributed energy resources at end users level, it has been recognized as key engineering challenge to redesign relays The technical difficulty lies how set up logic so that they could accurately detect faulty conditions. performance traditional protection settings are limited by insufficient fault current either due limit electronics or high...

The reliability of energy systems is strongly influenced by the prevailing climate conditions. With increasing prevalence renewable sources, interdependence between and has become even stronger. This study examines impact different spatial resolutions in modeling on grid assessment, with Texas interconnection 2033 2043 serving as a pilot case study. Our preliminary findings indicate that while low-resolution simulations can provide rough estimate system reliability, high-resolution more...

In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The leverages generative adversarial networks to create quasi-steady states the power system slowly-varying conditions and incorporates framework of neural ordinary differential equations (ODEs) capture transient behaviors during voltage oscillation events. A numerical example large grid suggests that can realistic PMU measurements...

In electromagnetic transient (EMT) simulation, 80-97% of the computational time is devoted to solving network equations. A key observation that sub-matrix representing interaction between two far-away groups buses usually sparse and can be approximated by a low-rank matrix. Based on this observation, we propose novel approximation method which permits O(N log N)-time matrix-vector multiplication for each solution step. Comprehensive numerical studies are conducted 39-bus system 179-bus from...

Abstract Unprecedented winter storms that hit across Texas in February 2021 have caused at least 4.5 million customers to experience load shedding due the wide-ranging generation capacity outage and record-breaking electricity demand. While much remains be investigated on what, how, why such wide-spread power outages occurred Texas, it is imperative for broader research community develop insights based a coherent electric grid model data set. In this paper, we collaboratively release an...

The self-adaptive sliding mode position controller based on genetic algorithm optimization is designed for the servo motor drive system. Firstly, researched to estimate magnitude of unknown disturbance in perturbed Then, adaptive used optimize ideal parameters and switching parameters. In end paper, several simulation experiments are carried out test performance results show that has better control tracking response system parameter variation external load disturbance.

Over the past several years electric power sector has been challenged by a number of extreme events around globe. Significant societal and economic shocks were due to rapid spread COVID-19 world. In addition pandemic, there have weather disruptions electricity sector, such as February 2021 Texas outage 9 p.m. nine-minute blackout event in India.

Eccentricity severity level estimation is of great importance in rotary machine fault detection. However, practice operation conditions may influence the magnitude signatures, making eccentricity a challenging problem. In this paper, we develop linear regression model incorporating multiple signature features to estimate induction machines under different operating conditions. particular, modeled as function and including rotating speed, load torque, vibration, well current harmonics, etc,...