Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

Medeiros, J.A.C.C.; Schirru, Roberto

doi:10.1016/j.anucene.2007.08.013

Cited by 56 publications

(4 citation statements)

References 2 publications

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“…Transient recognition is the supplement and development of fault diagnosis, there are a lot of methods of transient recognition. The current nuclear power plant transient identification methods are mostly based on data driven, mainly in the following three categories: neural network based transient recognition method [3],based on the statistical analysis of the transient recognition method based on fuzzy theory [4] and the transient recognition method [5]. As shown in Table 1.…”

Section: Analysis Of the Basic Methods Of Transient Recognitionmentioning

confidence: 99%

Analysis of Nuclear Power Plant Transient Recognition Method Validation Utility Strategy

Liang

Ding

2015

AMM

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Compare and analysis the transient identification in Nuclear Power Plant fault diagnosis method. According to how does these methods for nuclear power plant systems HTR effect, Further proposed to a problem that towards existing transient recognition of methods to verification effectiveness. Preliminary analysis of the three kinds of validation strategy, after a comparative demonstration, it is formed the preliminary research on the utility analysis framework. Utility to verify the framework for the Nuclear Power Plant fault diagnosis methods carry out intensive research provides a theoretical basis.

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Section: Analysis Of the Basic Methods Of Transient Recognitionmentioning

confidence: 99%

Analysis of Nuclear Power Plant Transient Recognition Method Validation Utility Strategy

Liang

Ding

2015

AMM

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“…Many meta-heuristic algorithms have been applied to parameter identification. For example, GA and PSO have been used for power system parameter identification problems (Quispe, Graciela 2008;Carlos, Schirru 2008).…”

Section: Parameter Identificationmentioning

confidence: 99%

Applications of Gravitational Search Algorithm in Engineering

Siddiquea

Adelib

2016

Journal of Civil Engineering and Management

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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems.

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“…Due to the simple philosophy of the way it works, the PSO has attracted the attention of many researchers from different areas. The reliability of the PSO has been demonstrated in many practical applications [2,3,4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic particle swarm optimization based on neighborhood rough set model

Miao

Shi

Zhang

et al. 2010

2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)

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To obtain the prior space information on the study problems and prevent the blind search, a novel strategy on the particle swarm optimization (PSO) is proposed. Based on the neighborhood rough set model, the prior information is achieved to guide the evolutionary state of the PSO constantly. By reserving the much relevant area of the global best point, the search space was dynamically reduced. Comparison studies with another improved PSO were performed.The experimental results for most test functions demonstrated good performance of the proposed method in both the optimization speed and computational accuracy. The results are firmly verified the effectiveness of the method to obtain the prior space information and improve the performance of the PSO.Index Terms-particle swarm optimization, rough set, swarm intelligence I.

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Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

Cited by 56 publications

References 2 publications

Analysis of Nuclear Power Plant Transient Recognition Method Validation Utility Strategy

Analysis of Nuclear Power Plant Transient Recognition Method Validation Utility Strategy

Applications of Gravitational Search Algorithm in Engineering

Dynamic particle swarm optimization based on neighborhood rough set model

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