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Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

Background Low back pain and musculoskeletal injuries (MSKIs) of the Spine are the most common reason for U.S. active duty service members (ADSMs) to seek medical care. The purposes of this descriptive study were to report the prevalence/incidence, health care utilization, and health care costs attributable to Spine (thoracic, lumbar, sacral, and pelvic) MSKIs across the four major branches of service from Fiscal Years (FY) 2016 to 2021. Materials and Methods This was a retrospective, longitudinal population study, including ADSMs from the Air Force, Army, Marine Corps, and Navy. Prevalence and incidence rates for Spine MSKIs, associated health care utilization, and costs were obtained by querying electronic health records (EHRs) from military treatment facilities, private sector care (PC) claims, and theater medical data from the Military Health System Data Repository from October 1, 2015 to September 30, 2021 (FY16–21). Utilization associated with Spine MSKIs in both the direct care (DC) and PC settings was classified into mutually exclusive outpatient encounter categories and acute inpatient stays. PC costs related to Spine MSKIs were captured per year. Results In FY21, 269,301 ADSMs sought care for Spine MSKI, representing 18.0% of the U.S. Armed Forces. The prevalence of Spine MSKIs ranged from 17.4 to 19.5% during FY16–21, with the highest annual prevalence among the Army (20.7–22.9%) and Air Force (19.0–22.6%). Across the study period, Soldiers had the highest share (47.8–50.4%) of DC outpatient encounters for Spine MSKI (primary or secondary diagnosis). The Air Force relied most heavily on PC for outpatient Spine MSKI care across the study period, where in FY21 Airmen accounted for 36.5% of the outpatient PC Spine MSKI encounters totaling $21,140,935 in PC costs. In FY21, total PC costs for Spine MSKI totaled $99,317,832. Conclusions This retrospective, descriptive study establishes prevalence/incidence, health care utilization, and PC costs for Spine MSKIs across the Services from FY16–21 and highlights the burden of Spine MSKIs among the U.S. Armed Forces, with costs amounting to over $99 million in PC reliance in FY21 alone. [ABSTRACT FROM AUTHOR]

Background Active duty service members (ADSMs) of the U.S. Armed Forces are uniquely at risk for musculoskeletal injuries (MSKIs) of the Head/Neck region, including the eye and face, from training with head gear, donning Kevlar, operating aircraft, and maintaining sitting or standing postures for prolonged durations. The purposes of this descriptive study were to report the prevalence/incidence, health care utilization, and health care costs attributable to Head/Neck MSKIs across the Services from fiscal years (FYs) 2016 to 2021. Methods This was a retrospective, longitudinal population study, including ADSMs from the Air Force, Army, Marine Corps, and Navy. Prevalence and incidence rates for Head/Neck MSKIs, associated health care utilization, and private sector costs were obtained by querying electronic health records from military treatment facilities, private sector care (PC) claims, and theater medical data from October 1, 2015 to September 30, 2021 (FYs 16–21), using the Military Health System Data Repository. Utilization associated with Head/Neck MSKIs in both the direct care and PC settings was classified into mutually exclusive outpatient encounter categories and acute inpatient stays. PC costs related to Head/Neck MSKIs were captured for each year. Results In FY21, 109,683 ADSMs sought care for Head/Neck MSKIs, representing 7.3% of the U.S. Armed Forces. The prevalence of Head/Neck MSKIs ranged from 6.9 to 7.8% during FY16–21, with the highest annual prevalence among the Air Force (8.0–9.4%) and Army (7.9–8.8%). Within direct care across the services, Soldiers presented for the highest proportion (45.9–47.9%) of outpatient encounters for Head/Neck MSKI annually. The Air Force relied most heavily on PC for outpatient Head/Neck MSKI care, which accounted for $9,134,741 in PC costs and comprised 37.2% of all PC encounters in FY21. Conclusions This retrospective, descriptive study established prevalence/incidence, health care utilization, and PC costs for Head/Neck MSKIs across the services from FY16–21, emphasizing the burden of Head/Neck MSKIs among the U.S. Armed Forces, with PC costs amounting to $42,912,940 in FY21 alone. [ABSTRACT FROM AUTHOR]

As the distribution grid moves toward a tightly monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on microphasor measurement unit (μPMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the μPMU devices in our architecture, an optimal μPMU placement with limited number of sensors is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real μPMU data.

We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors - such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals - such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machine-learning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.

There is significant interest in smart grid analytics based on phasor measurement data. One application is estima- tion of the Thevenin equivalent model of the grid from local measurements. In this paper, we propose methods using phasor measurement data to track Thevenin parameters at substations delivering power to both an unbalanced and balanced feeder. We show that for an unbalanced grid, it is possible to estimate the Thevenin parameters at each instant of time using only instantaneous phasor measurements. For balanced grids, we propose a method that is well-suited for online applications when the data is highly temporally-correlated over a short window of time. The effectiveness of the two methods is tested via simulation for two use-cases, one for monitoring voltage stability and the other for identifying cyber attackers performing "reconnaissance" in a distribution substation.

We review future global demand for electricity and major technologies positioned to supply itwith minimal greenhouse gas (GHG) emissions: renewables (wind, solar, water, geothermal and biomass), nuclear fission, and fossil power with CO2 capture and sequestration. Two breakthrough technologies (space solar power and nuclear fusion) are discussed as exciting but uncertain additional options for low net GHG emissions (“low-carbon”) electricity generation. Grid integration technologies (monitoring and forecasting of transmission and distribution systems, demand-side load management, energy storage, and load balancing with low-carbon fuel substitutes) are also discussed. For each topic, recent historical trends and future prospects are reviewed, along with technical challenges, costs and other issues as appropriate. While no technology represents an ideal solution, their strengths can be enhanced by deployment in combination, along with grid integration that forms a critical set of enabling technologies to assure a reliable and robust future low-carbon electricity system.

The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators has been widely documented. Micro-PMUs (µPMUs) are an emerging sensing technology that can provide similar benefits to Distribution System Operators (DSOs), enabling a level of visibility into the distribution grid that was previously unattainable. In order to support the deployment of these high resolution sensors, the automation of data analysis and prioritizing communication to the DSO becomes crucial. In this paper, we explore the use of µPMUs to detect anomalies on the distribution grid. Our methodology is motivated by growing concern about failures and attacks to distribution automation equipment. The effectiveness of our approach is demonstrated through both real and simulated data.

Electric power distribution systems are undergoing many technological changes and concerns are surfacing on possible additional vulnerabilities. Resilient cyber-physical systems (CPSs) in general must leverage state measures and operational models that interlink the physical and the cyber assets that compose them, to assess the global state. In this paper we describe a viable process of abstraction to obtain this holistic system state exploration tool, through the analysis of data from Micro Phasor Measurement Units (μPMUs) combined with the monitoring of Distribution Supervisory Control and Data Acquisition (DSCADA) traffic, and using semantics to interpret these data that expresses the specific system physical and operational constraints in both cyber and physical realms.