Particle Swarm Optimization Applied to Restoration of Electrical Energy Distribution Systems

Lambert‐Torres, Germano; Martins, Helga Gonzaga; Coutinho, Maurílio Pereira; Salomon, Camila Paes; Filgueiras, Leonardo Schilling

doi:10.1007/978-3-540-92137-0_26

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…The particle swarm optimization (PSO) is one of several algorithms that have been analyzed to optimize the TEP problem (Lambert- Torres et al 2008;Trelea 2003). Algorithm shows the basics of the PSO algorithm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Análisis de factibilidad del uso de GPU para mejorar la eficiencia de los algoritmos de optimización de metaheurísticas

2023

NOVASINERGIA

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Actualmente, varios problemas de optimización del mundo real han sido modelados matemáticamente. El proceso de modelado considera la mayor cantidad de información posible para proporcionar resultados válidos, y el modelo obtenido comúnmente se resuelve computacionalmente. Sin embargo, a medida que aumenta la información, también aumenta la complejidad. En consecuencia, se necesita una mayor capacidad computacional para resolver problemas complejos y escalables. Como resultado, se han desarrollado algoritmos meta-heurísticos para resolver problemas complejos de optimización. Estos algoritmos se usan comúnmente para dos o más dimensiones en las que están involucradas operaciones vectoriales y matriciales. Por lo tanto, es útil realizar procesos paralelos que reduzcan el tiempo de ejecución para solucionar este problema. Actualmente, las unidades centrales de procesamiento (CPU, por sus siglas en inglés) multinúcleo logran resolver fácilmente pequeños problemas con cálculos paralelos. Sin embargo, la unidad de procesamiento de gráficos (GPU, por sus siglas en inglés) mejora el rendimiento porque integra una cantidad de núcleos más importante que la CPU. Es muy útil para resolver problemas utilizando varios procesos en paralelo. Las operaciones matriciales, el problema del vendedor y el problema de planificación de expansión de la transmisión (TEP) han sido seleccionados para implementarse utilizando la GPU para verificar la contribución del procesador al rendimiento de los cálculos científicos. En los resultados, la GPU ayudó a resolver el "problema del vendedor" porque se analizaron más soluciones o partículas candidatas en menos tiempo. Debido a estos resultados, se asumió que habría un mejor rendimiento resolviendo el problema TEP utilizando la GPU y analizando un número mayor de topologías candidatas en menos tiempo. Sin embargo, este no fue el caso; según los resultados, el uso de la GPU lleva más tiempo al analizar más partículas.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Análisis de factibilidad del uso de GPU para mejorar la eficiencia de los algoritmos de optimización de metaheurísticas

2023

NOVASINERGIA

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“…Particle swarm optimization (PSO) is a stochastic algorithm that is normally used in continuous search spaces but can also be implemented in combinatorial optimization problems with a discrete search space. In infrastructure management, PSO has been previously applied to the electrical energy distribution systems by Lambert-Torres et al (2008), to maximize the supplied load and minimize the energy loss. Ahmed et al (2019) implemented a discrete particle swarm optimization (DPSO) algorithm for pavement maintenance activities, and Hu et al (2012) used PSO for an optimal allocation of earthquake emergency shelters.…”

Section: Introductionmentioning

confidence: 99%

Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization

Moghtadernejad

Adey

Hackl

2022

J. Infrastruct. Syst.

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One of the main challenges in the postdisaster management of large transportation networks involves the determination of the priority and the level of service recovery for each damaged asset in the network. Presently, the application of metaheuristic algorithms in developing restoration programs is receiving increasing attention. These algorithms determine a good solution to minimize the consequences of extreme events on the network of study in a relatively short period of time. This paper investigates the suitability of a discrete particle swarm optimization (DPSO) algorithm in finding a good solution to a restoration model developed for minimizing the overall direct and indirect costs of postdisaster restorative interventions. This model can consider constraints and limitations on the available budget, work groups and equipment, as well as different levels and speeds of service recovery for assets per damage state, and the changes in the traffic flow as the restorative interventions are executed. Moreover, the model has the capacity to process complex networks; hence, it can be implemented in realworld postdisaster decision making related to the development of restoration programs. The results suggest that the DPSO algorithm is a suitable choice of optimization algorithm in situations where the number of damaged objects is medium to large.

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“…Among these techniques, Particle Swarm Optimization (PSO) is highlighted, and is based on the behavior of bird's flocks in search for food [10]. PSO algorithms are used in functions optimization and are currently applied in several themes related to electrical power systems, such as: Optimal Power Flow [5,14], Power System Restoration [11] and Voltage and Reactive Power Control [12]. PSO algorithms can provide accurate convergence properties associated to simple implementation and reduced computational time [5].…”

Section: Introductionmentioning

confidence: 99%

Load flow computation via Particle Swarm Optimization

Salomon

Lambert‐Torres

Martins

et al. 2010

2010 9th IEEE/IAS International Conference on Industry Applications - INDUSCON 2010

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This paper proposes the application of a Particle Swarm Optimization (PSO) algorithm to the Load Flow calculation in Power Systems. The proposed methodology is based on the minimization of power mismatches in the system buses. The power flow study provides the system status in the steady-state and it is fundamental to the power system operation, planning and control. PSO is applied in a new computational model for the system power flow obtainment. This model searches for a better convergence, as well as a wider application in comparison with traditional methods as the Newton-Raphson method. This methodology was tested with numerical experiments accomplished in an IEEE 6-Bus System.

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Particle Swarm Optimization Applied to Restoration of Electrical Energy Distribution Systems

Cited by 9 publications

References 6 publications

Análisis de factibilidad del uso de GPU para mejorar la eficiencia de los algoritmos de optimización de metaheurísticas

Análisis de factibilidad del uso de GPU para mejorar la eficiencia de los algoritmos de optimización de metaheurísticas

Prioritizing Road Network Restorative Interventions Using a Discrete Particle Swarm Optimization

Load flow computation via Particle Swarm Optimization

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