4,837 research outputs found
Advanced flight control system study
The architecture, requirements, and system elements of an ultrareliable, advanced flight control system are described. The basic criteria are functional reliability of 10 to the minus 10 power/hour of flight and only 6 month scheduled maintenance. A distributed system architecture is described, including a multiplexed communication system, reliable bus controller, the use of skewed sensor arrays, and actuator interfaces. Test bed and flight evaluation program are proposed
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Reconfiguration of power networks based on graph-theoretic algorithms
The intentional area partitioning and automated distribution system restoration are two important Smart Grid technologies to enhance the robustness of a power network and improve the system reliability. In this dissertation, the research work is focused on deriving and implementing efficient graph-theoretic algorithms to analyze and solve such two real-world problems in power systems as follows.
In response to disturbances, a self-healing system reconfiguration that splits a power network into self-sufficient islands can stop the propagation of disturbances and avoid cascading events. An area partitioning algorithm that minimizes both real and reactive power imbalance between generation and load within islands is proposed. The proposed algorithm is a smart grid technology that applies a highly efficient multilevel multi-objective graph partitioning technique. The simulation results obtained on a 200- and a 22,000- bus test systems indicate that the proposed algorithm improves the voltage profile of an island after the system reconfiguration compared with the algorithm that only considers real power balance. In doing so, the algorithm maintains the computational efficiency.
The distribution system restoration is aimed at restoring loads after a fault by altering the topological structure of the distribution network by changing open/closed states of some tie switches and sectionalizing switches in the distribution system. A graph-theoretic distribution system restoration strategy that maximizes the amount of load to be restored and minimizes the number of switching operations is developed. Spanning tree based algorithms are applied to find the candidate restoration strategies. Unbalanced three-phase power flow calculation is performed to guarantee that the proposed system topology meets all electrical and operational constraints. Simulation results obtained from realistic feeder models demonstrate the effectiveness of the proposed approach
Optimal operations and resilient investments in steam networks
Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the networkâs resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience
An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach
A magnetic levitation system (MLS) is a complex nonlinear system that
requires an electromagnetic force to levitate an object in the air. The
electromagnetic field is extremely sensitive to noise which can cause the
acceleration on the spherical object, leading it to move into the unbalanced
region. This paper presents a comparative assessment of controllers for the
magnetic levitation system using proportional integral derivative (PID)
controller based optimal tuning. The analysis was started by deriving the
mathematical model followed by the implementation of radial basis function
neural network (RBFNN) based metamodel. The optimal tuning of the PID
controller has offered better transient responses with the improvement of
overshoot and the rise time as compared to the standard optimization
methods. It is more robust and tolerant as compared to gradient descent
method. The simulation output using the radial basis based metamodel
approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are
better than the gradient descent (GD) and conventional PID methods. For the
verification purpose, a Simscape model has been developed which mimic the
real model. It was found that the model has produced about similar
performance as what has been obtained from the Matlab simulation
Reinforcement Learning and Game Theory for Smart Grid Security
This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and failures, design different gametheoretic approaches to identify the critical components vulnerable to attack and propose their associated defense strategy, and utilizes machine learning techniques to solve the game-theoretic problems in adversarial and collaborative adversarial power grid environment. Our contributions can be divided into three major parts:Vulnerability identification: Power grid outages have disastrous impacts on almost every aspect of modern life. Despite their inevitability, the effects of failures on power gridsâ performance can be limited if the system operator can predict and identify the vulnerable elements of power grids. To enable these capabilities we study machine learning algorithms to identify critical power system elements adopting a cascaded failure simulator as a threat and attack model. We use generation loss, time to reach a certain percentage of line outage/generation loss, number of line outages, etc. as evaluation metrics to evaluate the consequences of threat and attacks on the smart power grid.Adversarial gaming in power system: With the advancement of the technologies, the smart attackers are deploying different techniques to supersede the existing protection scheme. In order to defend the power grid from these smart attackers, we introduce an adversarial gaming environment using machine learning techniques which is capable of replicating the complex interaction between the attacker and the power system operators. The numerical results show that a learned defender successfully narrows down the attackersâ attack window and reduce damages. The results also show that considering some crucial factors, the players can independently execute actions without detailed information about each other.Deep learning for adversarial gaming: The learning and gaming techniques to identify vulnerable components in the power grid become computationally expensive for large scale power systems. The power system operator needs to have the advanced skills to deal with the large dimensionality of the problem. In order to aid the power system operator in finding and analyzing vulnerability for large scale power systems, we study a deep learning technique for adversary game which is capable of dealing with high dimensional power system state space with less computational time and increased computational efficiency. Overall, the results provided in this dissertation advance power gridsâ resilience and security by providing a better understanding of the systemsâ vulnerability and by developing efficient algorithms to identify vulnerable components and appropriate defensive strategies to reduce the damages of the attack
Multilevel Converters: An Enabling Technology for High-Power Applications
| Multilevel converters are considered today as the
state-of-the-art power-conversion systems for high-power and
power-quality demanding applications. This paper presents a
tutorial on this technology, covering the operating principle and
the different power circuit topologies, modulation methods,
technical issues and industry applications. Special attention is
given to established technology already found in industry with
more in-depth and self-contained information, while recent
advances and state-of-the-art contributions are addressed with
useful references. This paper serves as an introduction to the
subject for the not-familiarized reader, as well as an update or
reference for academics and practicing engineers working in
the field of industrial and power electronics.Ministerio de Ciencia y TecnologĂa DPI2001-3089Ministerio de EduaciĂłn y Ciencia d TEC2006-0386
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