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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]

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]

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]

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]

This study addresses the issue of identifying the most suitable retail electric provider plan for the residential customers that are consumers only as well as consumers who intend to become prosumers. The main objective is to develop a methodology that will assist the residential customers in identifying the most suitable distributed energy resources, such as rooftop solar photovoltaic and home battery storage as well as electric vehicles. Furthermore, the proposed methodology is used to develop a decision support tool that aims to determine the appropriate retail electric provider plan that will ensure maximum savings on the customer's energy bill. The results of implementing the proposed methodology on case studies from Texas have shown that the residential customers can achieve significant savings if they install rooftop solar photovoltaic. The results have also shown that the maximum savings are usually associated with the time-variant or time-invariant plans, in case of customers with low energy usage, reaching up to $221/year. On the other hand, the customers with high energy usage were found to achieve significant savings on their energy bills when they are on the tiered time-invariant plans, in which case the savings may reach up to $475/year. [ABSTRACT FROM AUTHOR]

Active flux model of permanent magnet synchronous motors simplifies the model and eases sensorless control implementation. However, incorporating the current model (CM) that depends on the estimated electrical position for active flux estimation may cause performance degradation. The conventional active flux observer analysis revealed that position error influences sensorless control performance, especially during transient conditions. Thus, two improvement strategies named method-1 and method-2 are proposed in this article. Method-1 is based on an adaptive gain observer-based approach designed to discard the effect of position error. The observer gains are dependent on the estimated reference frame currents, which leads to the observer complexity. Method-2, on the other hand, uses the position error from the estimated active flux to obtain the true CM active flux without changing the conventional observer gains. Thus, the effect of position error on active flux estimation is discarded. Method-1 and method-2 fully decoupled the speed-position identification and the active flux observer. Meanwhile, a feedforward compensated phase-locked loop is adopted for speed-position identification. The proposed sensorless control strategies have been extensively validated using the Texas digital signal processor (TMS320F28335) and RT-LAB real-time simulator. The experimental results indicated that the proposed methods improved the sensorless control and active flux estimation performance. [ABSTRACT FROM AUTHOR]

We propose an approach to solving the AC network-constrained unit commitment problem (AC-NCUC) of large-scale power systems. This approach relies on solving convex (continuous and mixed-integer) optimization problems. First, we formulate a second-order conic relaxation of the original optimization problem, which renders a mixed-integer second-order conic optimization problem. To solve this problem, we use Benders’ decomposition, which decomposes it into a master problem, which is mixed-integer linear, and a subproblem, which is continuous and second-order conic. To improve the convergence of the Benders’ algorithm, we add several linear feasibility constraints to the master problem. Since the second-order relaxation of the original problem cannot guarantee AC feasibility of the solution found, we ensure such feasibility by solving a sequence of continuous convex optimization problems. We numerically validate the proposed approach using a Texas 2000-bus system. [ABSTRACT FROM AUTHOR]

A 20-GHz fractional- ${N}$ analog phase-locked loop (PLL) leveraging a novel high-speed charge pump (CP) and an on-chip frequency reference is demonstrated. An on-chip fully integrable 2.5-GHz frequency reference using Texas Instrument’s indigenous bulk acoustic wave (BAW) resonator is demonstrated. The low noise high-frequency reference allows for significant lowering of the division modulus leading to enhanced suppression of CP, phase-frequency detector (PFD), and loop-filter (LF) noise. A low noise class-C transformer-coupled voltage-controlled oscillator (VCO) further allows for excellent jitter performance over wide integration bandwidths (BWs) while still working with a nominally low PLL loop BW. Capability is built into the design to characterize the PLL with either BAW or external reference. The design is implemented and fabricated in the GlobalFoundries 22-nm fully depleted silicon on insulator (FD-SOI) process. The class-C VCO is measured to be centered at $\sim $ 19.7 GHz with 16% tuning range (TR) while maintaining a flat $\vert {\rm FOM}\vert \sim $ 188 dBc/Hz (10-MHz offset) over the entire TR. The PLL measures an excellent jitter and $\vert {\rm FOM}_{j}\vert $ of 65/92 fs and $\sim $ 249/245 dB in integer/fractional modes, respectively. [ABSTRACT FROM AUTHOR]

In this article, a new frequency adaptive second-order disturbance observer (FASODO) for back electromotive force (EMF) and active flux estimation is proposed using the active flux model of interior permanent magnet synchronous motors. FASODO is a frequency adaptive approach and eliminates the phase delay compensator demand of the traditional sliding mode observer-based back EMF estimator. Besides, FASODO estimates the active flux and the back EMF simultaneously. As a result, a quadrature phase-locked loop (Q-PLL) using either the back EMF or the active flux and a tan inverse approach can be used for speed–position identification. Meanwhile, an integrally compensated Q-PLL is designed to overcome the traditional Q-PLL position estimation error during ramp frequency tracking. The design guideline of FASODO and the integrally compensated Q-PLL are provided. A comparative analysis with traditional SMO shows that the proposed approach can deliver a better sensorless control performance. Furthermore, a comparison with a frequency adaptive complex coefficient filter enhanced SMO shows a comparable result, while the proposed approach additionally delivers the active flux. The approach is verified using the Texas Digital Signal Processor (TMS320F28335) and RT-LAB Real-Time Simulator, taking into account different operating conditions. [ABSTRACT FROM AUTHOR]