Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer

Nguyen, Hung Duc; Pham, Ly Huu

doi:10.11591/eei.v12i5.5245

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…The dandelion optimization algorithm is a new optimization algorithm, which is based on the diffusion process of dandelions in nature and finds the optimal solution through population synergy [20]. Compared with other algorithms, the dandelion algorithm has the advantages of fast convergence and easy implementation [21,22]. Therefore, the dandelion algorithm has a wide application prospect in solving single-objective optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Tang,

Cao,

Chen

et al. 2024

Biomimetics

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In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

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Section: Related Workmentioning

confidence: 99%

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Tang,

Cao,

Chen

et al. 2024

Biomimetics

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Identification of Transformer Parameters Using Dandelion Algorithm

El-Dabah,

Agwa

2024

ASI

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Researchers tackled the challenge of finding the right parameters for a transformer-equivalent circuit. They achieved this by minimizing the difference between actual measurements (currents, powers, secondary voltage) during a transformer load test and the values predicted by the model using different parameter settings. This process considers limitations on what values the parameters can have. This research introduces the application of a new and effective optimization algorithm called the dandelion algorithm (DA) to determine these transformer parameters. Information from real-time tests (single- and three-phase transformers) is fed into a computer program that uses the DA to find the best parameters by minimizing the aforementioned difference. Tests confirm that the DA is a reliable and accurate tool for estimating the transformer parameters. It achieves excellent performance and stability in finding the optimal values that precisely reflect how a transformer behaves. The DA achieved a significantly lower best fitness function value of 0.0136101 for the three-phase transformer case, while for the single-phase case it reached 0.601764. This indicates a substantially improved match between estimated and measured electrical parameters for the three-phase transformer model. By comparing DA with six competitive algorithms to prove how well each method minimized the difference between measurements and predictions, it could be shown that the DA outperforms these other techniques.

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Red-Billed Blue Magpie Optimizer for Electrical Characterization of Fuel Cells with Prioritizing Estimated Parameters

El-Fergany,

Agwa

2024

Technologies

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The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision.

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Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer

Cited by 3 publications

References 26 publications

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Identification of Transformer Parameters Using Dandelion Algorithm

Red-Billed Blue Magpie Optimizer for Electrical Characterization of Fuel Cells with Prioritizing Estimated Parameters

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