PWR power distribution flattening using Quantum Particle Swarm intelligence

Jamalipour, Mostafa; Gharib, Morteza; Sayareh, Reza; Khoshahval, Farrokh

doi:10.1016/j.anucene.2013.01.026

Cited by 22 publications

(4 citation statements)

References 11 publications

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“…Primarily, it was intended to simulate social behaviour. In nuclear area, PSO has been used to investigate to the nuclear reactor reload optimization problem [15], PWR power distribution flattening, and critical heat flux prediction [16]. Combination of backpropagation neural networks and PSOhas been investigated in development of nuclear reactor control [17].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Based Probabilistic Neural Network for Classification of Severe Accident of Nuclear Reactor

Pambudi

2021

Tri Dasa Mega

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Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard probabilistic neural network (PNN) and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB. The experiment results showed that supervised PNN PSO could classify severe accident of nuclear power plant better than the standar PNN.

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Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Based Probabilistic Neural Network for Classification of Severe Accident of Nuclear Reactor

Pambudi

2021

Tri Dasa Mega

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“…QPSO bases on DELTA, considering that the particles have quantum behavior. In quantum space, the particles make stochastic optimization search in the whole feasible solution space, so QPSO has stronger global optimization capability than standard PSO [17]. QPSO algorithm makes use of the wave function t  （X, ） to describe the state of the particles, obtains probability density function particles appear in a certain point by solving the Schrödinger Equation and gets particles position Eq.…”

Section: Overview Of Qpsomentioning

confidence: 99%

Optimal placement and sizing of wind / solar based DG sources in distribution system

Guan

Guo

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract.Proper placement and sizing of Distributed Generation (DG) in distribution system can obtain maximum potential benefits .This paper proposes quantum particle swarm algorithm (QPSO) based wind turbine generation unit (WTGU) and photovoltaic (PV) array placement and sizing approach for real power loss reduction and voltage stability improvement of distribution system. Performance modeling of wind and solar generation system are described and classified into PQ\PQ (V)\PI type models in power flow. Considering the WTGU and PV based DGs in distribution system is geographical restrictive, the optimal area and DG capacity limits of each bus in the setting area need to be set before optimization, the area optimization method is proposed . The method has been tested on IEEE 33-bus radial distribution systems to demonstrate the performance and effectiveness of the proposed method. IntroductionDistributed generation (DG) in power system networks has rapidly increased as the demand of power system is growing exponentially. DG is small-scale power generation and usually located in distribution network [1]. DG units are mainly energized by wind, solar and fuel cell and have many advantages over centralized power generation. The optimum DG placement and sizing at planning stage of distribution system is necessary to achieve the reduction of power system losses and improved voltage profile [2].However, installing DG units at non-optimal place may get an opposite effect to what is desired. Selecting the best places for installing DG units and their preferable sizes in large distribution systems is a complex combinatorial optimization problem.The optimal placement and sizing of DG in distribution networks has been continuously studied in order to achieve the maximum benefits. J.O. Kim [3] presented the improved Hereford Ranch algorithm to solve the DG allocation problem and getting significant effect. An analytical approach put forward by A Naresh to allocate DG for minimizing the total power losses in primary distribution network [4], DG is placed one by one in the network to obtain minimum power loss of the system. Authors in [5] considered a simple conventional iterative search technique along with NR (Newton Raphson) method of load flow study is implemented. But the analytical approach is not suitable for the large scale network for its large amount of calculation. GA [6] and PSO [7] algorithm, as the representative of the artificial intelligence algorithm, are applied to optimal the place and size of DG. Ref. [8] combined discrete

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Section: Introductionmentioning

confidence: 99%

“…The quantum space is the whole feasible solution space, which satisfies the wave principle of quantum mechanics. The particle has the characteristics of uncertainty in the search space and it can search in the whole feasible solution space, therefore, we conclude that the quantum particle swarm optimization algorithm has such advantages as the strong global search ability, etc [4], [5].Based on QPSO algorithm, this article introduces a new search strategy. During the search process, each particle no longer updates its own position only by learning its current local optimal value and global optimal value, but by learning its current local optimal value and other particles' current local optimal value and global optimal value.…”

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confidence: 99%

Quantum Particle Swarm Optimization Algorithm Based on Dynamic Adaptive Search Strategy

Huo

2014

TELKOMNIKA

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The particle swarm system simulates the evolution of the social mechanism. In this system, the individual particle representing the potential solution flies in the multidimensional space in order to find the better or the optimal solution. But because of the search path and limited speed, it's hard to avoid local best and the premature phenomenon occurs easily. Based on the uncertain principle of the quantum mechanics, the global search ability of the quantum particle swarm optimization (QPSO) Keywords: quantum theory, particle swarm optimization algorithm, search strategy IntroductionPSO algorithm is a new evolutionary computation technology, which belongs to the category of swarm intelligence algorithm. For PSO algorithm, the movement of particles fully embodies the characteristics of the swarm algorithm, and during the process of the movement, particles follow the optimal position found by them and the one of the entire population, eventually making the whole particle swarm gather at the optimal solution position. The particle swarm algorithm is simple in the concept and easy in adjusting parameters, so it has been widely used. However, because of the search path and limited speed, it's hard to avoid local best and the premature phenomenon occurs easily [1]. The emergence of the quantum particle swarm optimization algorithm solves the problem of limited search scope. Based on the uncertain principle of the quantum mechanics, the global search ability of the quantum particle swarm optimization algorithm is better than the particle swarm algorithm [2].For traditional PSO algorithms, the particle searches by flying. The flying process depends on the speed, however, with limited speed, the particle can only search within a limited search scope, and the limited search scope limits the particle in a fixed area without covering the whole feasible solution. Thus, the particle swarm can't search the global optimal solution with probability 1. QPSO bases on DELTA potential well model to determine that the particle state is similar with the quantum behavior [3]. For quantum particle swarm optimization algorithm, the particle state is determined by wave function. The quantum space is the whole feasible solution space, which satisfies the wave principle of quantum mechanics. The particle has the characteristics of uncertainty in the search space and it can search in the whole feasible solution space, therefore, we conclude that the quantum particle swarm optimization algorithm has such advantages as the strong global search ability, etc [4], [5].Based on QPSO algorithm, this article introduces a new search strategy. During the search process, each particle no longer updates its own position only by learning its current local optimal value and global optimal value, but by learning its current local optimal value and other particles' current local optimal value and global optimal value. This paper first introduces the basic particle swarm optimization algorithm, and then expounds the quantum theory, the principle ...

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PWR power distribution flattening using Quantum Particle Swarm intelligence

Cited by 22 publications

References 11 publications

Particle Swarm Optimization Based Probabilistic Neural Network for Classification of Severe Accident of Nuclear Reactor

Particle Swarm Optimization Based Probabilistic Neural Network for Classification of Severe Accident of Nuclear Reactor

Optimal placement and sizing of wind / solar based DG sources in distribution system

Quantum Particle Swarm Optimization Algorithm Based on Dynamic Adaptive Search Strategy

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