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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

A method of analysing the small signal stability of very large power systems is described. The method is based on a frequency domain approach which concentrates on the electromechanical modes of the Power System under study. By using a modular modelling concept and a very efficient sparsity network solution routine, systems having up to 12000 buses and 1000 detailed generator models may be studied. Comparison is made between eigenvalues calculated by the new method, and other existing programs.

Eigenvalue analysis of very large power systems

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

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.

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

Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. This paper proposes a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in real-time. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system. The results show that the proposed approach can detect low margin attacks (as low as 1% of actual measurements) with up to 99% accuracy. All of the developed source codes of the proposed solution are publicly available at Github. https://github.com/nbhusal/Data-Attack-on-Voltage-Regulation .

/pdf/detection-of-cyber-attacks-on-voltage-regulation-in-1redbm0plm.pdf

Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning

In concept, a smart charging management system (SCMS) optimizes the charging of plug-in vehicles (PEVs) and provides various grid services including voltage control, frequency regulation, peak shaving, renewable energy integration support, spinning reserve, and emergency demand response. These functionalities largely depend upon data collected from various entities such as PEVs, electric vehicle supply equipment (EVSE), service providers, and utilities. SCMS can be susceptible to both cyber and physical threats (e.g. man-in-the-middle attack, data intrigued attack, denial of charging, physical-attack) due to interactions of and interdependencies between cyber and physical components. Cyber-physical threats through highly connected malware vectors raise various concerns including public safety hazards to vehicle operators and those in the immediate vicinity as well as disruptions to electric grid operations. This paper describes the concept of SCMS and provides a comprehensive review of the cybersecurity aspects of EVSEs and SCMSs with their possible impacts on the power grid and society. It also contributes to the development of cybersecurity measures to the SCMSs. Various functions of SCMS are reviewed in detail including peak shaving, demand charge reduction, frequency regulation, spinning reserve, renewable integration support, distribution congestion management, reactive power compensation, and emergency demand response with unidirectional PEVs charging. Also, a critical literature survey on current practices of SCMS cybersecurity is provided to explore major impacts and challenges of cyber-physical attacks and to identify research gaps and vulnerabilities in currently available SCMSs technologies.

Cybersecurity of Electric Vehicle Smart Charging Management Systems

This paper proposes a Lagrange multipliers-based adaptive under-frequency load shedding (UFLS) method for frequency control in power systems. UFLS is one of key factors for frequency stability especially in stressed power grids with high penetration of renewable energy generation and deregulated electricity market. Conventional UFLS methods usually shed fixed amount of predetermined loads at predetermined locations which can lead to over or under load curtailment. This paper presents an adaptive method, based on Lagrange multipliers of power balance constraints, to determine not only the amount of the load-step to be shed but also the best locations for load shedding. This method applies a two-stage strategy for load-shedding. In the first stage, the deficit/disturbance power is computed based on the initial rate of change of frequency. In the second stage, the optimization analysis is performed so as to calculate Lagrange multipliers, which are used in the determination of the location and the amount of load-step to be shed. The proposed method is implemented on several test systems including the reduced 9-bus 3-machine Western Electricity Coordinating Council (WECC) system and the New England 39-bus 10-machine system. The results show that the proposed UFLS approach can effectively bring the system back to normal state after contingencies.

Mukesh Gautam

Papers

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

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

Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning

Cybersecurity of Electric Vehicle Smart Charging Management Systems

A Sensitivity-based Approach to Adaptive Under-Frequency Load Shedding