Network Reliability (network + reliability)

Distribution by Scientific Domains


Selected Abstracts


Reliability evaluation of transmission network including effect of protection systems

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 8 2008
A.-R. Alesaadi
Abstract Transmission network reliability evaluation considering effects of protective systems is investigated in this paper. Protective system faults are one of the main sources of cascading outages and may lead to vast blackouts. Desired performance of the protection system has a significant role in the improvement of network reliability. The proposed method is based on minimal cutset approach. In the proposed method, all elements of the transmission and subtransmission substations can be considered in which subtransmission substations are taken as output nodes. A sensitivity analysis of network reliability to component reliability parameters is performed. Finally the effectiveness of the method was tested in a real network with satisfactory results. Copyright 2007 John Wiley & Sons, Ltd. [source]


Sensor network design for fault tolerant estimation

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2004
M. Staroswiecki
Abstract This paper addresses the problem of fault tolerant estimation and the design of fault tolerant sensor networks. Fault tolerance is defined with respect to a given estimation objective, namely a given functional of the system state should remain observable when sensor failures occur. Redundant and minimal sensor sets are defined and organized into an automaton which contains all the subsets of sensors such that the estimation objective can be achieved. Three criteria, which evaluate the system fault tolerance with respect to sensor failures when a reconfiguration strategy is used, are introduced: (strong and weak) redundancy degrees (RD), sensor network reliability (R), and mean time to non-observability (MTTNO). Sensor networks are designed by finding redundant sensor sets whose RD and/or R and/or MTTNO are larger than some specified values. A ship boiler example is developed for illustration. Copyright 2004 John Wiley & Sons, Ltd. [source]


A novel steady-state genetic algorithm approach to the reliability optimization design problem of computer networks

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, Issue 1 2009
A. M. Mutawa
This paper introduces the development and implementation of a new methodology for optimizing reliability measures of a computer communication network within specified constraints. A genetic algorithm approach with specialized encoding, crossover, and mutation operators to design a layout topology optimizing source-terminal computer communication network reliability is presented. In this work, we apply crossover at the gene level in conjunction with the regular chromosome-level crossover operators that are usually applied on chromosomes or at boundaries of nodes. This approach provides us with a much better population mixture, and hence faster convergence and better reliability. Applying regular crossover and mutation operators on the population may generate infeasible chromosomes representing a network connection. This complicates fitness and cost calculations, since reliability and cost can only be calculated on links that actually exist. In this paper, a special crossover and mutation operator is applied in a way that will always ensure production of a feasible connected network topology. This results in a simplification of fitness calculations and produces a better population mixture that gives higher reliability rates at shorter convergence times. Copyright 2008 John Wiley & Sons, Ltd. [source]


Partition-based algorithm for estimating transportation network reliability with dependent link failures

JOURNAL OF ADVANCED TRANSPORTATION, Issue 3 2008
Agachai Sumalee
Evaluating the reliability of a transportation network often involves an intensive simulation exercise to randomly generate and evaluate different possible network states. This paper proposes an algorithm to approximate the network reliability which minimizes the use of such simulation procedure. The algorithm will dissect and classify the network states into reliable, unreliable, and un-determined partitions. By postulating the monotone property of the reliability function, each reliable and/or unreliable state can be used to determine a number of other reliable and/or unreliable states without evaluating all of them with an equilibrium assignment procedure. The paper also proposes the cause-based failure framework for representing dependent link degradation probabilities. The algorithm and framework proposed are tested with a medium size test network to illustrate the performance of the algorithm. [source]


State-space partition techniques for multiterminal flows in stochastic networks

NETWORKS: AN INTERNATIONAL JOURNAL, Issue 2 2006
Matthew S. Daly
Abstract This article develops state-space partition methods for computing performance measures for stochastic networks with demands between multiple pairs of nodes. The chief concern is the evaluation of the probability that there exist separate, noninteracting flows that satisfy all demands. This relates to the multiterminal maximum flow problem discussed in the classic article of Gomory and Hu. The network arcs are assumed to have independent, discrete random capacities. We refer to the probability that all demands can be satisfied as the network reliability (with the understanding that its definition is application dependent). In addition, we also consider the calculation of secondary measures, such as the probability that a particular subset of demands can be met, and the probability that a particular arc lies on a minimum cut. The evaluation of each of these probabilities is shown to be NP-hard. The proposed methods are based on an iterative partition of the system state space, with each iteration tightening the bounds on the measure of interest. This last property allows the design of increasingly efficient Monte Carlo sampling plans that yield substantially more precise estimators than the standard Monte Carlo method that draws samples from the original capacity distribution. 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(2), 90,111 2006 [source]