Choice of Selection Methods in Genetic Algorithms for Power System State Estimation

Tran, Thanh-Son; Kieu, T.T.H.

doi:10.1007/978-3-030-64719-3_26

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…The genetic algorithm (GA) uses a selection operator (Boltzmann selection, Tournament selection, Rank selection, etc.) 86 to allocate probabilities to individuals based on their fitness values. It allows for the selection of individuals to create the next generation in proportion to their fitness values.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Deghfel,

Badoud,

Merahi

et al. 2024

Sci Rep

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Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Deghfel,

Badoud,

Merahi

et al. 2024

Sci Rep

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show abstract

“…Among the three operators used in this paper, the selection operator is Roulette wheel selection because it is most suitable for PSSE problems (Tran and Kieu, 2021). The two others are heuristic crossover and uniform mutation.…”

Section: Problem Formulation and Proposed Approachmentioning

confidence: 99%

“…However, it could only solve the problem of the systems, having no more than six buses. Tran and Kieu (2021) studied the 5-bus and 14-bus systems and suggested the best choice of selection operator for GA.…”

Section: Introductionmentioning

confidence: 99%

Decoupling of voltage magnitude and phase in genetic algorithms for smart transmission network state estimation

Tran

Kieu

2023

Front. Energy Res.

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A large amount of renewable energy resources are integrated into electricity transmission networks. For efficient operation, transmission networks should be smart, in which it is necessary to have the function of estimating the state of the power system. This function determines bus voltage magnitude and phase, which are used for monitoring, operating, and controlling transmission networks. Transmission network state estimation is developed to estimate bus voltage magnitude and phase by many methods, which include genetic algorithms. Published articles have not mentioned in detail how to apply genetic algorithms to estimate the state of large transmission networks, and the application is limited to small networks. Therefore, this work seeks to estimate the state of large transmission networks by genetic algorithms. Strong decoupling was observed between active power and the voltage phase, as well as between reactive power and voltage magnitude, while a weak coupling, between active power and voltage magnitude, as well as between reactive power and voltage phase; voltage magnitude has a value of around 1.0 p.u, and the voltage phase has a value of around 0.0. This paper proposes a novel approach to genetic algorithms for estimating the state of smart transmission networks. In this novel approach, voltage magnitude and phase are separately estimated at each iteration of genetic algorithms. The approach is validated on IEEE 14-, 30-, and 118-bus networks. Results show that our approach can use phasor measurement unit data to estimate the state of large smart transmission networks by genetic algorithms.

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“…Several selection approaches are available for selecting the parents, such as: Roulette Wheel, Tournament, Rank-based, Elitism selection techniques [8]. Many studies have been conducted to address this issue, and it is found that no single selection approach was superior to the others in general [9][10][11]. Thus, the choice of the selection method primarily depends on a specific problem.…”

Section: Iintroductionmentioning

confidence: 99%

Improved Genetic algorithm for fuel loading optimization of the DNRR with HEU fuel

Phan,

Tran,

2023

Nucl. Sci. and Tech.

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This paper investigates the performance of genetic algorithm (GA) with improved selection techniques, i.e. Tournament and Roulette Wheel, applied to in-core fuel management of the Dalat nuclear research reactor (DNRR). Numerical calculations have been performed based on the DNRR core with 100 HEU fuel bundles. The optimal fitness function was chosen to maximize the keff and minimize the power peaking factor. The statistical analysis using Mann-Whitney test shows that the performance of GA with Tournament selection is advantageous over the Roulette Wheel selection in the ICFM problem of the DNRR. The optimal core configurations obtained with the improved GA methods have the keff values greater by about 500 pcm, and the PPF lower by about 4.0% compared to the reference core.

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Choice of Selection Methods in Genetic Algorithms for Power System State Estimation

Cited by 4 publications

References 17 publications

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Decoupling of voltage magnitude and phase in genetic algorithms for smart transmission network state estimation

Improved Genetic algorithm for fuel loading optimization of the DNRR with HEU fuel

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