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In 2017, three sub-synchronous resonance (SSR) events were reported in the transmission system of Electric Reliability Council of Texas (ERCOT). These three events with different consequences are due to the same cause, i.e., Type-3 wind farms radially connected to a series compensated transmission line. The objectives of this paper are: 1) to build a testbed to replicate these real-world events and investigate what causes the different consequences; 2) to find out the impact of wind penetration level and wind speed on SSR; and 3) to investigate how to mitigate SSR. The replication testbed is built in MATLAB/Simpowersystems with each wind farm represented by a single aggregated wind turbine with full dynamics. The challenge of replication is the limited information about those three events, the ERCOT system, and the wind farms. Nevertheless, relying on simplification and assumptions, we are able to replicate the three events. Our contributions include the configured testbed for Type-3 wind farm SSR studies, diagnosis of impacting factors on SSR, and the confirmation of an SSR mitigation strategy through grid-side converter ac voltage command signal modulation. [ABSTRACT FROM AUTHOR]

This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security constrained economic dispatch (SCED). Prior work has shown that FDI attacks can be designed via an attacker-defender bi-level linear program (ADBLP) to cause physical overflows after re-dispatch using DCOPF. In this paper, it is shown that attacks designed using DCOPF fail to cause overflows on $N-1$ reliable systems because the system response modeled is inaccurate. An ADBLP that accurately models the system response is proposed to find the worst-case physical consequences, thereby modeling a strong attacker with system level knowledge. Simulation results on the synthetic Texas system with 2000 buses show that even with the new enhanced attacks, for systems operated conservatively due to $N-1$ constraints, the designed attacks only lead to post-contingency overflows. Moreover, the attacker must control a large portion of measurements and physically create a contingency in the system to cause consequences. Therefore, it is conceivable but requires an extremely sophisticated attacker to cause physical consequences on $N-1$ reliable power systems operated with RTCA and SCED. [ABSTRACT FROM AUTHOR]

This article focuses on the 2004 IEEE Microwave Theory and Techniques Society (IEEE MTT-S) International Microwave Symposium (IMS), which was held during 6-11 June 2004, in Fort Worth, Texas. 970 papers were originally submitted to the IEEE MTT-S IMS Technical Program Committee in December 2003 for possible presentation at the symposium in June 2004. Of those papers, 488 were accepted at the IEEE MTT-S IMS. Each of authors was then invited to expand their papers. 126 expanded papers were submitted and of these 65 papers have been accepted for publication. Of these, 22 were published in the November 2004 issue of the journal "IEEE Transactions on Microwave Theory and Techniques."

Power grids have recently become more susceptible to sub-synchronous control interactions (SSCI) due to the increasing penetration of renewable resources and series-compensated lines. SSCI is a purely electrical and fast-growing phenomenon. Therefore, without an expeditious detection and mitigation solution, it can cause serious damage to renewable and transmission facilities, consequently leading to cascading outages. This paper proposes an effective algorithm to detect SSCI within 45 ms and reliably send a trip signal within 95 ms. First, a comprehensive SSCI analysis is suggested to improve the preprocessing of the measured signal. Then, the proposed algorithm utilizes mode identification approaches in conjunction with a moving fast fourier transform (FFT) to capture and process the frequency and magnitude of oscillations to issue a reliable trip command in the case of an SSCI event. To evaluate the algorithm, a portion of the Electric Reliability Council of Texas (ERCOT) grid with relatively high penetration of renewable generation resources is utilized. The proposed detection algorithm is implemented on a real-time target device and is tested with an SSCI signal from the ERCOT grid under real-time operating conditions. The results indicate the efficiency and accuracy of the method with successful detection within the acceptable time frame. [ABSTRACT FROM AUTHOR]

In this paper, we present a method (Action-Fusion) for human action recognition from depth maps and posture data using convolutional neural networks (CNNs). Two input descriptors are used for action representation. The first input is a depth motion image that accumulates consecutive depth maps of a human action, whilst the second input is a proposed moving joints descriptor which represents the motion of body joints over time. In order to maximize feature extraction for accurate action classification, three CNN channels are trained with different inputs. The first channel is trained with depth motion images (DMIs), the second channel is trained with both DMIs and moving joint descriptors together, and the third channel is trained with moving joint descriptors only. The action predictions generated from the three CNN channels are fused together for the final action classification. We propose several fusion score operations to maximize the score of the right action. The experiments show that the results of fusing the output of three channels are better than using one channel or fusing two channels only. Our proposed method was evaluated on three public datasets: 1) Microsoft action 3-D dataset (MSRAction3D); 2) University of Texas at Dallas-multimodal human action dataset; and 3) multimodal action dataset (MAD) dataset. The testing results indicate that the proposed approach outperforms most of existing state-of-the-art methods, such as histogram of oriented 4-D normals and Actionlet on MSRAction3D. Although MAD dataset contains a high number of actions (35 actions) compared to existing action RGB-D datasets, this paper surpasses a state-of-the-art method on the dataset by 6.84%. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Stochastic optimization can be used to model predictable but uncertain element failures, in an attempt to enhance system reliability in power system operation and planning. In practical applications, such as preventive operation during severe weather, the uncertainty set is often very large. This will lead to two challenges: (i) every possible scenario cannot be practically identified; and (ii) the computational demands of stochastic optimization with a large scenario set cannot be met. To address these challenges, this paper develops a multidimensional scenario selection method, which creates a rather small but representative set of scenarios. The developed method makes use of failure features as well as network features of the elements that may fail, to achieve a superior performance. The simulations studies on a synthetic large-scale Texas system, show the dominant performance of the method compared to existing algorithms in the literature, as well as common industry practices. Due to its effectiveness, the method presented in this paper enables computationally efficient implementation of stochastic power system operation and planning software tools. Such stochastic tools will improve system reliability and efficiency through enhanced use of the existing resources, without requiring any expensive system upgrade. [ABSTRACT FROM AUTHOR]

As renewable energy penetration increases and system inertia levels drop, primary frequency control is becoming a critical concern in relatively small interconnections such as the Electric Reliability Council of Texas (ERCOT). To address this problem ERCOT is implementing a number of market rule changes including the introduction of a new Fast Frequency Response (FFR) reserve type to the electricity market. This FFR reserve type aims to help the traditional Primary Frequency Response (PFR) reserve type in arresting frequency decline in the event of a large generator outage. This paper derives reserve requirements to ensure sufficient reserve to arrest frequency decline before reaching the critical frequency threshold while coupling PFR reserve, FFR reserve, and system inertia. The general reserve requirement places limits on the amount of PFR reserve that can be provided by each unit based on its ramping capabilities. Two such limits are derived from first principles and another is proposed that is capable of accommodating the equivalency ratio introduced in previous work. These PFR reserve limits also provide first principles insight into equivalency ratios, which have only been studied empirically in the past. High-level insights are provided on a large Texas test case. [ABSTRACT FROM AUTHOR]

Oscillations have been observed in wind farms with weak grid interconnections. While Texas observes 4 Hz low-frequency oscillations, the west region in China observes subsynchronous oscillations at 30 Hz. Furthermore, this oscillation mode caused torsional interactions with a remote synchronous generator and led to shutdown of the power plant. Inspired by those real-world events, this paper aims to present an analytical model of type-4 wind in weak grids that can demonstrate both low-frequency and subsynchronous frequency oscillations. Critical factors, e.g., the parameters of the phase-locked loop (PLL), are examined using small-signal analysis. The analysis results are validated using a testbed built in MATLAB/SimPowerSystems. Except power electronic switching sequences, the testbed has controls (e.g., wind turbine pitch control, maximum power point control, converter controls), machine dynamics and power system dynamics modeled. This testbed includes a 100 MW Type-4 wind farm, a 600 MW synchronous generator, a long transmission line and a grid. The testbed successfully demonstrates two types of dominant oscillations under different PLLs. In addition, the testbed demonstrates torsional interactions due to the proximity of the subsynchronous mode and one of the torsional modes. [ABSTRACT FROM AUTHOR]

Large power consumers often have on-site generation. If the utility tie opens, it is essential to isolate the on-site generators to prevent generator equipment damage before the utility attempts to reclose in a possible out-of-synchronism condition. Typically, islanding detection schemes are used to detect this condition and disconnect on-site generation quickly, thereby allowing successful utility reclose. This paper introduces a reliable islanding detection solution that was proposed, implemented, and tested at a refinery in Texas. The facility houses two on-site generators and is interconnected to the utility via a looped system. A topology-based islanding detection scheme was originally installed at this facility but was later decommissioned because of difficulties in adapting to evolving topology changes. Following an undesirable event in the utility system that could have resulted in damage to the refinery on-site generator equipment, the refinery decided to implement an islanding detection scheme that is independent of system topology changes and only relies on local-area measurements. However, the dependability of a traditional local measurement-based scheme is vulnerable to the dynamic generation–load mismatch, making it possibly unreliable. This paper describes a solution using a redundant high-speed multiprinciple islanding detection scheme that incorporates a unique voting supervision logic to achieve security. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven algorithm for locating the source of forced oscillations and suggests a physical interpretation for the method. By leveraging the sparsity of forced oscillations along with the low-rank nature of synchrophasor data, the problem of source localization under resonance conditions is cast as computing the sparse and low-rank components using Robust Principal Component Analysis (RPCA), which can be efficiently solved by the exact Augmented Lagrange Multiplier method. Based on this problem formulation, an efficient and practically implementable algorithm is proposed to pinpoint the forced oscillation source during real-time operation. Furthermore, theoretical insights are provided for the efficacy of the proposed approach, by use of physical model-based analysis, specifically by highlighting the low-rank nature of the resonance component matrix. Without the availability of system topology information, the proposed method can achieve high localization accuracy in synthetic cases based on benchmark systems and real-world forced oscillations in the power grid of Texas. [ABSTRACT FROM AUTHOR]

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for futurepandemic events. [ABSTRACT FROM AUTHOR]

Presents information on the 2021 IEEE Texas Symposium on Wireless & Microwave Circuits and Systems. [ABSTRACT FROM AUTHOR]

This paper suggests evolving linear models as a powerful alternative for online forecasting of synchronous time series. First, using a priori knowledge of an expert, all known possible features are clustered in some categories so that each category includes homogeneous features. Then, a novel evolving correlation-based forward subset selection technique is used to determine relevant features from each category. Next, based on the selected features, an estimation of the output of the system is modeled through an evolving adaptive linear regression model. In evolving systems, selected features and their associated weights could vary over time based on new incoming data samples which contain new information. Finally, a soft combination of output estimations of all categories, in a new sense of Takagi–Sugeno fuzzy system, gives the prediction of the output of the system at each sampling time. The approach offers a certain new view at the enhancement of evolving forecasting models. The proposed approach embodies recursive learning and one-step-ahead incremental algorithms that progressively modify the model to ensure continuous learning, and self-organization of the model structure and its parameters. Two real-world problems, forecasting electricity load of the Electric Reliability Council of Texas region and stock price forecasting of technology sector of Standard & Poor’s 500 index, are provided to validate the developed method. Pros and cons of the proposed approach are comprehensively discussed and shown through simulation results and comparisons with other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper develops models for hurricane exposure, fragility curve-based damage to electrical transmission grid components and power generating stations using Monte-Carlo simulation, and restoration cost to predict resiliency factors including power generation capacity lost and the restoration cost for electrical transmission grid and power generation system damages. Synthetic grid data are used to model the Energy Reliability Council of Texas (ERCOT) electrical grid. A case study is developed based on Hurricane Harvey. This work is extended to evaluate the changes to resiliency as the percentage of renewable sources is increased from 2017 levels to levels corresponding to the NREL Futures Study 2050 Texas scenarios for 50% and 80% renewable energy. [ABSTRACT FROM AUTHOR]

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. [ABSTRACT FROM AUTHOR]

Information about the 19th International Conference on Microelectronic Test Structures (ICMTS) held in Austin, Texas in 2006 is presented. There were over 40 regular papers presented in nine sessions at the meeting. The papers feature work directed to furthering the understanding of systematic and parametric yield loss characteristics of complementary metal oxide semiconductors (CMOS) to microelectromechanical systems (MEMS).

The article offers information on the Institute of Electrical and Electronics Engineers (IEEE) Computer Society and the periodical "IEEE Transactions on Mobile Computing." It notes that the IEEE Computer Society is a group of individuals who have a professional interest in the field of computers. The society publishes the periodical monthly, costing 20 U.S. dollars for IEEE members and 54 dollars for nonmembers per copy. Also provided is the list of the members of the editorial board, including Sajal K. Das of the University of Texas in Arlington.