868 resultados para Reactive programming Asynchronous stream ReactiveX RxJS RxPHP
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Optimised placement of control and protective devices in distribution networks allows for a better operation and improvement of the reliability indices of the system. Control devices (used to reconfigure the feeders) are placed in distribution networks to obtain an optimal operation strategy to facilitate power supply restoration in the case of a contingency. Protective devices (used to isolate faults) are placed in distribution systems to improve the reliability and continuity of the power supply, significantly reducing the impacts that a fault can have in terms of customer outages, and the time needed for fault location and system restoration. This paper presents a novel technique to optimally place both control and protective devices in the same optimisation process on radial distribution feeders. The problem is modelled through mixed integer non-linear programming (MINLP) with real and binary variables. The reactive tabu search algorithm (RTS) is proposed to solve this problem. Results and optimised strategies for placing control and protective devices considering a practical feeder are presented. (c) 2007 Elsevier B.V. All rights reserved.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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This paper presents a comparison of reactive power support in distribution networks provided by switched Capacitor Banks (CBs) and Distributed Generators (DGs). Regarding switched CBs, a Tabu Search metaheuristic algorithm is developed to determine their optimal operation with the objective of reducing the power losses in the lines on the system, while meeting network constraints. on the other hand, the optimal operation of DGs is analyzed through an evolutionary Multi-Objective (MO) programming approach. The objectives of such approach are the minimization of power losses and operation cost of the DGs. The comparison of the reactive power support provided by switched CBs and DGs is carried out using a modified IEEE 34 bus distribution test system.
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A combined methodology consisting of successive linear programming (SLP) and a simple genetic algorithm (SGA) solves the reactive planning problem. The problem is divided into operating and planning subproblems; the operating subproblem, which is a nonlinear, ill-conditioned and nonconvex problem, consists of determining the voltage control and the adjustment of reactive sources. The planning subproblem consists of obtaining the optimal reactive source expansion considering operational, economical and physical characteristics of the system. SLP solves the optimal reactive dispatch problem related to real variables, while SGA is used to determine the necessary adjustments of both the binary and discrete variables existing in the modelling problem. Once the set of candidate busbars has been defined, the program implemented gives the location and size of the reactive sources needed, if any, to maintain the operating and security constraints.
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In this paper a heuristic technique for solving simultaneous short-term transmission network expansion and reactive power planning problem (TEPRPP) via an AC model is presented. A constructive heuristic algorithm (CHA) aimed to obtaining a significant quality solution for such problem is employed. An interior point method (IPM) is applied to solve TEPRPP as a nonlinear programming (NLP) during the solution steps of the algorithm. For each proposed network topology, an indicator is deployed to identify the weak buses for reactive power sources placement. The objective function of NLP includes the costs of new transmission lines, real power losses as well as reactive power sources. By allocating reactive power sources at load buses, the circuit capacity may increase while the cost of new lines can be decreased. The proposed methodology is tested on Garver's system and the obtained results shows its capability and the viability of using AC model for solving such non-convex optimization problem. © 2011 IEEE.
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The optimal reactive dispatch problem is a nonlinear programming problem containing continuous and discrete control variables. Owing to the difficulty caused by discrete variables, this problem is usually solved assuming all variables as continuous variables, therefore the original discrete variables are rounded off to the closest discrete value. This approach may provide solutions far from optimal or even unfeasible solutions. This paper presents an efficient handling of discrete variables by penalty function so that the problem becomes continuous and differentiable. Simulations with the IEEE test systems were performed showing the efficiency of the proposed approach. © 1969-2012 IEEE.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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In this paper, the optimal reactive power planning problem under risk is presented. The classical mixed-integer nonlinear model for reactive power planning is expanded into two stage stochastic model considering risk. This new model considers uncertainty on the demand load. The risk is quantified by a factor introduced into the objective function and is identified as the variance of the random variables. Finally numerical results illustrate the performance of the proposed model, that is applied to IEEE 30-bus test system to determine optimal amount and location for reactive power expansion.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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A new approach called the Modified Barrier Lagrangian Function (MBLF) to solve the Optimal Reactive Power Flow problem is presented. In this approach, the inequality constraints are treated by the Modified Barrier Function (MBF) method, which has a finite convergence property: i.e. the optimal solution in the MBF method can actually be in the bound of the feasible set. Hence, the inequality constraints can be precisely equal to zero. Another property of the MBF method is that the barrier parameter does not need to be driven to zero to attain the solution. Therefore, the conditioning of the involved Hessian matrix is greatly enhanced. In order to show this, a comparative analysis of the numeric conditioning of the Hessian matrix of the MBLF approach, by the decomposition in singular values, is carried out. The feasibility of the proposed approach is also demonstrated with comparative tests to Interior Point Method (IPM) using various IEEE test systems and two networks derived from Brazilian generation/transmission system. The results show that the MBLF method is computationally more attractive than the IPM in terms of speed, number of iterations and numerical conditioning. (C) 2011 Elsevier B.V. All rights reserved.
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The integrated production scheduling and lot-sizing problem in a flow shop environment consists of establishing production lot sizes and allocating machines to process them within a planning horizon in a production line with machines arranged in series. The problem considers that demands must be met without backlogging, the capacity of the machines must be respected, and machine setups are sequence-dependent and preserved between periods of the planning horizon. The objective is to determine a production schedule to minimise the setup, production and inventory costs. A mathematical model from the literature is presented, as well as procedures for obtaining feasible solutions. However, some of the procedures have difficulty in obtaining feasible solutions for large-sized problem instances. In addition, we address the problem using different versions of the Asynchronous Team (A-Team) approach. The procedures were compared with literature heuristics based on Mixed Integer Programming. The proposed A-Team procedures outperformed the literature heuristics, especially for large instances. The developed methodologies and the results obtained are presented.
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Mainstream hardware is becoming parallel, heterogeneous, and distributed on every desk, every home and in every pocket. As a consequence, in the last years software is having an epochal turn toward concurrency, distribution, interaction which is pushed by the evolution of hardware architectures and the growing of network availability. This calls for introducing further abstraction layers on top of those provided by classical mainstream programming paradigms, to tackle more effectively the new complexities that developers have to face in everyday programming. A convergence it is recognizable in the mainstream toward the adoption of the actor paradigm as a mean to unite object-oriented programming and concurrency. Nevertheless, we argue that the actor paradigm can only be considered a good starting point to provide a more comprehensive response to such a fundamental and radical change in software development. Accordingly, the main objective of this thesis is to propose Agent-Oriented Programming (AOP) as a high-level general purpose programming paradigm, natural evolution of actors and objects, introducing a further level of human-inspired concepts for programming software systems, meant to simplify the design and programming of concurrent, distributed, reactive/interactive programs. To this end, in the dissertation first we construct the required background by studying the state-of-the-art of both actor-oriented and agent-oriented programming, and then we focus on the engineering of integrated programming technologies for developing agent-based systems in their classical application domains: artificial intelligence and distributed artificial intelligence. Then, we shift the perspective moving from the development of intelligent software systems, toward general purpose software development. Using the expertise maturated during the phase of background construction, we introduce a general-purpose programming language named simpAL, which founds its roots on general principles and practices of software development, and at the same time provides an agent-oriented level of abstraction for the engineering of general purpose software systems.
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In questa tesi ci si pone l'obiettivo di sviluppare sistemi distribuiti composti da device mobile che si scambiano informazioni tramite comunicazioni opportunistiche wireless peer-to-peer. Vengono inizialmente analizzate le principali tecnologie di comunicazione wireless adatte allo scopo, soffermandosi sulle reti Wifi ad hoc, delle quali vengono studiate le performance in sistemi di larga scala tramite il simulatore di reti ns-3. Successivamente viene esposto lo sviluppo di componenti software, basati su Akka Stream, per la costruzione di campi computazionali tramite comunicazioni opportunistiche tra device Android, effettuate tramite reti Wifi ad hoc.
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We describe an extension of the theory of Owicki and Gries (1976) to a programming language that supports asynchronous message passing based on unconditional send actions and conditional receive actions. The focus is on exploring the fitness of the extension for distributed program derivation. A number of experiments are reported, based on a running example problem, and with the aim of exploring design heuristics and of streamlining derivations and progress arguments.
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Nucleic Acid hairpins have been a subject of study for the last four decades. They are composed of single strand that is
hybridized to itself, and the central section forming an unhybridized loop. In nature, they stabilize single stranded RNA, serve as nucleation
sites for RNA folding, protein recognition signals, mRNA localization and regulation of mRNA degradation. On the other hand,
DNA hairpins in biological contexts have been studied with respect to forming cruciform structures that can regulate gene expression.
The use of DNA hairpins as fuel for synthetic molecular devices, including locomotion, was proposed and experimental demonstrated in 2003. They
were interesting because they bring to the table an on-demand energy/information supply mechanism.
The energy/information is hidden (from hybridization) in the hairpin’s loop, until required.
The energy/information is harnessed by opening the stem region, and exposing the single stranded loop section.
The loop region is now free for possible hybridization and help move the system into a thermodynamically favourable state.
The hidden energy and information coupled with
programmability provides another functionality, of selectively choosing what reactions to hide and
what reactions to allow to proceed, that helps develop a topological sequence of events.
Hairpins have been utilized as a source of fuel for many different DNA devices. In this thesis, we program four different
molecular devices using DNA hairpins, and experimentally validate them in the
laboratory. 1) The first device: A
novel enzyme-free autocatalytic self-replicating system composed entirely of DNA that operates isothermally. 2) The second
device: Time-Responsive Circuits using DNA have two properties: a) asynchronous: the final output is always correct
regardless of differences in the arrival time of different inputs.
b) renewable circuits which can be used multiple times without major degradation of the gate motifs
(so if the inputs change over time, the DNA-based circuit can re-compute the output correctly based on the new inputs).
3) The third device: Activatable tiles are a theoretical extension to the Tile assembly model that enhances
its robustness by protecting the sticky sides of tiles until a tile is partially incorporated into a growing assembly.
4) The fourth device: Controlled Amplification of DNA catalytic system: a device such that the amplification
of the system does not run uncontrollably until the system runs out of fuel, but instead achieves a finite
amount of gain.
Nucleic acid circuits with the ability
to perform complex logic operations have many potential practical applications, for example the ability to achieve point of care diagnostics.
We discuss the designs of our DNA Hairpin molecular devices, the results we have obtained, and the challenges we have overcome
to make these truly functional.