20 research outputs found
Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This paper presents a spatiotemporal unsupervised feature learning method for
cause identification of electromagnetic transient events (EMTE) in power grids.
The proposed method is formulated based on the availability of
time-synchronized high-frequency measurement, and using the convolutional
neural network (CNN) as the spatiotemporal feature representation along with
softmax function. Despite the existing threshold-based, or energy-based events
analysis methods, such as support vector machine (SVM), autoencoder, and
tapered multi-layer perception (t-MLP) neural network, the proposed feature
learning is carried out with respect to both time and space. The effectiveness
of the proposed feature learning and the subsequent cause identification is
validated through the EMTP simulation of different events such as line
energization, capacitor bank energization, lightning, fault, and high-impedance
fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the
WSCC 9-bus system.Comment: 9 pages, 7 figure
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Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
Background
Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.
Methods
22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.
Findings
Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.
Interpretation
Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic
Adaptive single-phase auto-reclosing method using power line carrier signals
This paper proposes a new method for adaptive single-phase auto-reclosing (ASPAR) that improves the rate of successful reclosing actions after the occurrence of a transient single-phase fault and prevents unsuccessful reclosing. After the occurrence of a fault, the only faulty phase is disconnected by the protection system (e.g., distance relays and circuit breakers). The proposed method deploys power line carrier (PLC) signals for determination of the secondary arc extinction time and releasing the reclosing signals to the circuit breakers. Despite the growing use of new communication systems (e.g., fibre-optic links), PLC systems are still widely used, and may not be considered for replacement in near future. Therefore, the proposed method can be utilized as an auxiliary application of PLC systems to enhance the resiliency of power grids. The simulation studies are carried out using EMTP-RV and MATLAB, and the advantages and disadvantages of the proposed method are discussed and compared with the existing ASPAR methods. According to the simulation results, the efficiency of the proposed ASPAR method is negligibly influenced by the following factors: fault location, faulty phase, system loading, transmission line transposition, shunt reactor, PLC carrier frequency, PLC operating mode, and the noises. © 2017 Elsevier Lt
A Recursive Method for Traveling-Wave Arrival-Time Detection in Power Systems
This paper proposes a novel recursive method for detecting the first arrival time (AT) of traveling waves (TWs) in power grids to enhance the fault-location methods relying on TWs. This method depends on the adaptive discrete Kalman filter. It estimates the parameters of a high time-resolution voltage or current measurement and generates residuals (innovation sequence). Both measurement noises and TWs can result in an abrupt change in the residuals. The proposed method pinpoints the probable abrupt change and distinguishes whether it is caused by noises or arriving waves. As the proposed method is recursive, it is proper for implementation in on-site digital fault locators for real-time applications. For evaluation of the proposed method, EMTP-RV and the real-time digital simulator are utilized to perform the transient simulations. The results are then analyzed in MATLAB. The proposed method and the state-of-the-art AT-detection methods in the prior literature are compared, and the sensitivity analysis demonstrates that the measurement noises and fault parameters have less influence on the proposed method efficiency in comparison to the existing AT-detection methods. © 1986-2012 IEEE
Decentralized Control Framework for Mitigation of the Power-Flow Fluctuations at the Integration Point of Smart Grids
In this paper, a decentralized control framework for reducing power-flow fluctuations at the integration point of DC smart microgrids (SMGs) is proposed. The output powers of non-dispatchable renewable energy resources are unpredictable and vary time to time. In this work, plug-in electric vehicles (PEVs) are employed as distributed energy storage systems (DESSs) in order to minimize the power-flow fluctuations at the integration point. In this regards, the proposed control system increases the charging rates of PEVs in excess power generation and reduces the charging rates in power shortage. The simulations are performed using Matlab/Simulink. According to the simulation results, the proposed method is able to lessen the fluctuations. It also reduces the dependency of SMGs on the main grid and improves the overall power quality in the main power systems as it minimizes the integration point power-flow fluctuations. © 2019 IEEE
Reducing Smart Microgrid Dependency on the Main Grid using Electric Vehicles and Decentralized Control Systems
International audienceThis paper proposes a new control system to reduce the power flow at the integration point of DC smart microgrids (SMGs) equipped with non-dispatchable renewable energy resources. The control system is fully decentralized, and it is based on the cooperative control, which requires the minimal communication infrastructure. In the proposed method, plug-in electric vehicles are utilized as distributed energy storage systems to mitigate the power-flow fluctuation. The PEVs start charging in excess of generation, and discharge in generation shortage. The proposed method decreases the dependency of SMGs on the main grid. It also improves the overall power quality in the bulk power systems by minimizing the integration point power-flow fluctuations. The proposed control system is evaluated using Matlab/Simulink. According to the simulation results, the performance of the proposed method is assessed, and its pros and cons are discussed
A Distributed Control System for Enhancing Smart-grid Resiliency using Electric Vehicles
This paper presents a decentralized control system based on cooperative control for managing the discharging rates of plug-in electric vehicles (PEVs) during islanding mode. Since energy storage systems (ESSs) are essential for the microgrids relying on renewable energies, it is assumed that the smart microgrid (SMG) is equipped with one main ESS. It is also assumed that several PEVs are connected to the grid via bidirectional controllable chargers, considering the proliferation of PEVs. In case of islanding, the proposed method controls the discharging rates of the PEVs to decrease the output power of the main ESS. This leads to an enhancement in the grid ride-through-ability, and consequently, its resiliency since the SMG can longer supply the loads in islanding mode. The proposed method is evaluated utilizing Matlab/Simulink. According to the simulation results, the advantages and disadvantages of the proposed control system are presented and discussed. © 2019 IEEE