Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm

Taher, Seyed Abbas; Afsari, Seyed Ahmadreza

doi:10.1155/2012/838629

Cited by 46 publications

(27 citation statements)

References 34 publications

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“…Differential Evolution with Gaussian perturbation, the function contours was taken by Taher and Afsari [24] 3 Experiments…”

Section: Self-adaptive Gaussian Perturbationmentioning

confidence: 99%

“…The CAs were generated by the Covering Array Library (CAS) [16] the National Institute of Standards and Technology (NIST). [24] To compare the results obtained by different schemes, mutation was performed by a nonparametric Friedman test [7] with a significance level of 99 % or p-value less than 0.1. Tables 2, 3 The results for two-dimensions are in Table 2, we can see that the median reported is practically the best for each of the test functions, and the standard deviation almost equals to zero, which means that each one of the 51 independent tests almost reached the same result.…”

Section: Self-adaptive Gaussian Perturbationmentioning

confidence: 99%

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Differential Evolution with Self-adaptive Gaussian Perturbation

Sotelo-Figueroa

Hernández-Aguirre

Espinal

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

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Differential evolution is a population-based metaheuristic that is widely used in Black-Box Optimization. The mutation is the main search operator and there are different implementation schemes reported in state of art literature. Nonetheless, such schemes lack mechanisms for an intensification stage, which can enable better search and avoid local optima. This article proposes a way to adapt the Covariance Matrix parameter of a Gaussian distribution that is used to generate a disturbance that improves the performance of two well-known mutation schemes. This disturbance allows working with problems with correlated variables. The test was performed over the CEC 2013 instances and the results were compared through the Friedman nonparametric test.

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“…Differential Evolution with Gaussian perturbation, the function contours was taken by Taher and Afsari [24] 3 Experiments…”

Section: Self-adaptive Gaussian Perturbationmentioning

confidence: 99%

Section: Self-adaptive Gaussian Perturbationmentioning

confidence: 99%

Differential Evolution with Self-adaptive Gaussian Perturbation

Sotelo-Figueroa

Hernández-Aguirre

Espinal

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

View full text Add to dashboard Cite

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“…The 33-node radial distribution network was used to test the algorithms for the energy losses and cost minimization [30]- [32]. The specifications of the distribution network are presented in Table 1.…”

Section: Radial Distribution Networkmentioning

confidence: 99%

“…Fig. 5 shows the diagram of the 33-node radial distribution network [30], [31]. The load considered was 3715 kW and 2300 kVAr, and the power generated was considered to be 3926 kW and 2443 kVAr.…”

Section: Radial Distribution Networkmentioning

confidence: 99%

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Comparación de algoritmos multiobjetivo inspirados en búsqueda armónica, búsqueda cuco y murciélagos para la ubicación de generación distribuida renovable

Candelo-Becerra

Riaño

Santander-Mercado

2015

TecnoL.

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Electric power losses have a significant impact on the total costs of distribution networks. The use of renewable energy sources is a major alternative to improve power losses and costs, although other important issues are also enhanced such as voltage magnitudes and network congestion. However, determining the best location and size of renewable energy generators can be sometimes a challenging task due to a large number of possible combinations in the search space. Furthermore, the multiobjective functions increase the complexity of the problem and metaheuristics are preferred to find solutions in a relatively short time. This paper evaluates the performance of the cuckoo search (CS), harmony search (HS), and bat-inspired (BA) algorithms for the location and size of renewable distributed generation (RDG) in radial distribution networks using a multiobjective function defined as minimizing the energy losses and the RDG costs. The metaheuristic algorithms were programmed in Matlab and tested using the 33-node radial distribution network. The three algorithms obtained similar results for the two objectives evaluated, finding points close to the best solutions in the Pareto front. Comparisons showed that the CS obtained the minimum results for most points evaluated, but the BA and the HS were close to the best solution. KeywordsCuckoo search, bat-inspired algorithm, renewable distributed generation, harmony search, photovoltaic energy systems, wind energy systems. ResumenLas pérdidas eléctricas tienen un impacto significativo en los costos totales de las redes de distribución. El uso de las energías renovables es una gran alternativa para mejorar las pérdidas y los costos, aunque también otros problemas en las magnitudes de las tensiones y la congestión de la red pueden ser mejorados. Sin embargo, determinar la mejor localización y dimensionamiento de generadores eléctricos renovables puede ser a veces una tarea difícil debido al gran número de combinaciones posibles existentes en el espacio de búsqueda. Además, el uso de funciones multiobjetivo incrementa la complejidad del problema y se prefiere usar las metaheurísticas para encontrar soluciones en un tiempo relativamente corto. En este trabajo se evalúa el desempeño de los algoritmos inspirados en búsqueda cuco, búsqueda armónica y murciélagos para la localización y dimensionamiento de la generación distribuida renovable en redes de distribución radiales, usando funciones como la minimización de las pérdidas de energía y los costos de la generación distribuida renovable. Las metaheurísticas fueron programadas en Matlab y evaluadas usando la instancia denominada red de distribución radial de 33 nodos. Los tres algoritmos evaluados obtuvieron resultados similares para los dos objetivos evaluados, encontrando Frentes de Pareto cercanos a las mejores soluciones. La comparación realizada mostró que la búsqueda cuco obtiene los mejores resultados, pero los algoritmos inspirados en murciélagos y búsqueda armónica obtuvieron resultados cercanos a la mejor soluci...

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