다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

비만아 부모의 돌봄 경험: q 방법론

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Ensemble deep learning: A review

This paper describes two feeder reconfiguration algorithms for the purpose of service restoration and load balancing in a real-time operation environment. The developed methodologies combine optimization techniques with heuristic rules and fuzzy logic for efficiency and robust performance. Many of practical operating concerns of feeder reconfiguration and the coordination with other distribution automation applications are also addressed. The developed algorithms have been implemented as 8 production grade software. Test results on PG&E distribution feeders show that the performance is efficient and robust.

Distribution Feeder Reconfiguration for Service Restoration and Load Balancing

Recently, there has been a focus on natural and man-made disasters with a high-impact low-frequency (HILF) property in electric power systems. A power system must be built with “resilience” or the ability to withstand, adapt and recover from disasters. The resilience metrics (RMs) are tools to measure the resilience level of a power system, normally employed for resilience cost–benefit in planning and operation. While numerous RMs have been presented in the power system literature; there is still a lack of comprehensive framework regarding the different types of the RMs in the electric power system, and existing frameworks have essential shortcomings. In this paper, after an extensive overview of the literature, a conceptual framework is suggested to identify the key variables, factors and ideas of RMs in power systems and define their relationships. The proposed framework is compared with the existing ones, and existing power system RMs are also allocated to the framework’s groups to validate the inclusivity and usefulness of the proposed framework, as a tool for academic and industrial researchers to choose the most appropriate RM in different power system problems and pinpoint the potential need for the future metrics.

Power Systems Resilience Metrics: A Comprehensive Review of Challenges and Outlook

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. This calls for developing control and operation methods and planning strategies to improve grid resilience against such events. The first step toward this goal is to develop resilience metrics and evaluation methods to compare planning and operation alternatives and to provide techno-economic justifications for resilience enhancement. Although several power system resilience definitions, metrics, and evaluation methods have been proposed in the literature, they have not been universally accepted or standardized. This paper provides a comprehensive and critical review of current practices of power system resilience metrics and evaluation methods and discusses future directions and recommendations to contribute to the development of universally accepted and standardized definitions, metrics, evaluation methods, and enhancement strategies. This paper thoroughly examines the consensus on the power system resilience concept provided by different organizations and scholars and existing and currently practiced resilience enhancement methods. Research gaps, associated challenges, and potential solutions to existing limitations are also provided.

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Power System Resilience: Current Practices, Challenges, and Future Directions

A convolutional neural network-based approach to composite power system reliability evaluation

Deep ensemble learning-based approach to real-time power system state estimation

This paper proposes a voltage constrained-based approach to determine the maximum hosting capacity of electric distribution systems (DSs) to electric vehicles (EVs). The continuous growth in penetration level of EVs in DSs increases the challenges to allocate power system resources. Determining the maximum hosting capacity of EVs in DSs without violating voltage constraints has a potential to handle these challenges. In this work, the maximum hosting capacity of DSs to EVs is determined-under both uncontrolled (actual) and controlled charging scenarios-ensuring that voltage requirements are not violated. The maximum hosting capacity of EVs under the uncontrolled charging scenario is estimated based on load profile of EVs and hourly extra available power (HEAP). EV load profiles are constructed using probability distribution functions of daily travel distance, departure time, and arrival time. These distribution functions are constructed using survey data collected from several technical reports. The HEAP is calculated by taking the differences between the maximum hourly loading capacities and hourly loads at each node. For the fully controlled charging scenario, daily extra available energy (DEAE) of DSs and daily required energy to charge EVs are used to estimate the maximum hosting capacity of DSs. The DEAE of a distribution system is estimated using the HEAP. The proposed approach is demonstrated on the IEEE 123 test feeder through several case studies. OpnDSS is used to calculate the HEAP and to check the voltage constraints. Monte Carlo simulations are used to calculate the expected maximum hosting capacity. The results show that the maximum hosting capacities under uncontrolled and controlled charging scenarios are 438 and 1510 cars respectively.

Determining Maximum Hosting Capacity of Electric Distribution Systems to Electric Vehicles

The frequency of disruptive and newly emerging threats (e.g. man-made attacks—cyber and physical attacks; extreme natural events—hurricanes, earthquakes, and floods) has escalated in the last decade. Impacts of these events are very severe ranging from long power outage duration, major power system equipment (e.g. power generation plants, transmission and distribution lines, and substation) destruction, and complete blackout. Accurate modeling of these events is vital as they serve as mathematical tools for the assessment and evaluation of various operations and planning investment strategies to harden power systems against these events. This paper provides a comprehensive and critical review of current practices in modeling of extreme events, system components, and system response for resilience evaluation and enhancement, which is a important stepping stone toward the development of complete, accurate, and computationally attractive modeling techniques. The paper starts with reviewing existing technologies to model the propagation of extreme events and then discusses the approaches used to model impacts of these events on power system components and system response. This paper also discusses the research gaps and associated challenges, and potential solutions to the limitations of the existing modeling approaches.

Narayan Bhusal

Papers

Power System Resilience: Current Practices, Challenges, and Future Directions

A convolutional neural network-based approach to composite power system reliability evaluation

Deep ensemble learning-based approach to real-time power system state estimation

Determining Maximum Hosting Capacity of Electric Distribution Systems to Electric Vehicles

Modeling of Natural Disasters and Extreme Events for Power System Resilience Enhancement and Evaluation Methods