Genetic algorithm and universal generating function technique for solving problems of power system reliability optimization

Levitin, Gregory; Lisnianski, Anatoly; Haim, Hanoch Ben; Elmakis, David

doi:10.1109/drpt.2000.855730

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…Evolutio-nary algorithms, mainly, genetic algorithms (GAs) have been used in many works for solve reliability optimization pro-blems [20,22,29]. However, the tests performed in [7] showed that the particle swarm optimization algorithm (PSO) is able to get better results than those obtained by a GA for several reliability optimization problems.…”

Section: Particle Swarm Optimizationmentioning

confidence: 97%

Optimal cyclic replacement policy in MSS maintenance via Binomial-PSO

Zavala

Ramos

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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Several systems can perform their intended functions at more than two different levels, from perfectly working to completely failed. These kind of systems are known as multistate systems. In many complex and sophisticated systems, reliability and maintainability theory plays a very important role in maintaining such systems. The purpose of MSS maintenance optimization is to extend system lifetime, or at least the mean time to the next failure whose repair may be costly. Furthermore, it is expected that effective maintenance policies can increase system availability and reduce the consequences of unavailability (unaccomplished demand). In this study, we consider cyclic replacement policy in a preventive maintenance planning for a component subject to failure. This paper introduces a new approach based on PSO with constraints handling for MSS maintenance optimization problems. The universal generating function is applied in this work to evaluate MSS reliability. Our approach is tested in a real MSS maintenance problem in full measure in the state-of-the-art. The main advantage of the suggested approach is that it can be easily implemented in practice for maintenance of real MSS.

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Section: Particle Swarm Optimizationmentioning

confidence: 97%

Optimal cyclic replacement policy in MSS maintenance via Binomial-PSO

Zavala

Ramos

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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“…Reliability estimation and maintenance scheduling of critical systems with an inherent high risk, such as at power plants, is discussed by Green et al (2011), Miranda et al (2009 and Levitin et al (2000) among others. System criticality is expressed in terms of sensitivity to a drop in reliability below predefined threshold levels.…”

Section: Reliability Estimationmentioning

confidence: 99%

“…Miranda et al (2009) propose directly implementing evolutionary PSO (EPSO) to estimate system reliability. Levitin et al (2000) use the fact that power stations have different task performance levels in order to develop a multi-state system based on different reliability levels. Genetic algorithms (GAs) were proposed as a means for estimating reliability, thus making it possible to control the power station's operation.…”

Section: Reliability Estimationmentioning

confidence: 99%

A particle swarm model for estimating reliability and scheduling system maintenance

Puzis

Shirtz

Elovici

2014

Enterprise Information Systems

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Modifying data and information system components may introduce new errors and deteriorate the reliability of the system. Reliability can be efficiently regained with reliability centred maintenance, which requires reliability estimation for maintenance scheduling. A variant of the particle swarm model is used to estimate reliability of systems implemented according to the model view controller paradigm. Simulations based on data collected from an online system of a large financial institute are used to compare three component-level maintenance policies. Results show that appropriately scheduled component-level maintenance greatly reduces the cost of upholding an acceptable level of reliability by reducing the need in system-wide maintenance.

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“…The best way to evaluate the availability of seriesparallel MSS systems where the total system capacity must not underpass a specific instantaneous demand level according to the cumulative load curve and which depends on the nominal performance of the individually used components is to use the so called Ushakov-or u-transform [3] …”

Section: Universal Moment Generating Function (Umgf) -Fundamentalsmentioning

confidence: 99%

Budgetary and Redundancy Optimisation of Homogeneous Series-Parallel Systems Subject to Availability Constraints Using Matlab Implemented Genetic Computing

Chaaban

Schwarz

Börcsöck

2013

24th IET Irish Signals and Systems Conference (ISSC 2013)

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This paper deals with finding optimal structures and redundant safety designs with minimal costs for series-Parallel multi state system (MSS) configurations subject to availability constraints using genetic algorithms as optimisation mean, since these biologically inspired evolution concepts showed stability, powerfulness, and effectiveness in solving such complex combinatorial optimisation tasks. The routine has been written in Matlab and the tests have been performed using some test data belonging to already existing models (Levitin, Lisnianski, and Ouzineb).

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Genetic algorithm and universal generating function technique for solving problems of power system reliability optimization

Cited by 14 publications

References 17 publications

Optimal cyclic replacement policy in MSS maintenance via Binomial-PSO

Optimal cyclic replacement policy in MSS maintenance via Binomial-PSO

A particle swarm model for estimating reliability and scheduling system maintenance

Budgetary and Redundancy Optimisation of Homogeneous Series-Parallel Systems Subject to Availability Constraints Using Matlab Implemented Genetic Computing

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