Particle Swarm Optimization Applied to Restoration of Electrical Energy Distribution Systems

2008 ◽  
pp. 228-238 ◽  
Author(s):  
Germano Lambert-Torres ◽  
Helga Gonzaga Martins ◽  
Maurilio Pereira Coutinho ◽  
Camila Paes Salomon ◽  
Leonardo Schilling Filgueiras
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Distribution ◽  
Distribution Systems ◽  
Particle Swarm ◽  
Electrical Energy ◽  
Swarm Optimization

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

scholarly journals DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS

Irriga ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 798-817
Author(s):  
Saulo de Tarso Marques Bezerra ◽  
José Eloim Silva de Macêdo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Water Distribution ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computational Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Irrigation Network

DIMENSIONAMENTO DE REDES DE DISTRIBUIÇÃO DE ÁGUA MALHADAS VIA OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS     SAULO DE TARSO MARQUES BEZERRA1 E JOSÉ ELOIM SILVA DE MACÊDO2   1 Universidade Federal de Pernambuco, Campus Agreste, Núcleo de Tecnologia, Avenida Campina Grande, S/N, Bairro Nova Caruaru, CEP 55014-900, Caruaru, Pernambuco, Brasil. [email protected]. 2 Centro Universitário Maurício de Nassau, Departamento de Engenharia Civil, BR 104, Km 68, S/N, Bairro Agamenon Magalhães, CEP 55000-000, Caruaru, Pernambuco, Brasil. [email protected].     1 RESUMO   Apresenta-se, neste trabalho, um modelo de otimização para o dimensionamento de sistemas pressurizados de distribuição de água para projetos de irrigação. A metodologia empregada é fundamentada no algoritmo Otimização por Enxame de Partículas (PSO), que é inspirada na dinâmica e comportamento social observados em muitas espécies de pássaros, insetos e cardumes de peixes. O PSO proposto foi aplicado em dois benchmark problems reportados na literatura, que correspondem à Hanoi network e a um sistema de irrigação localizado na Espanha. O dimensionamento resultou, para as mesmas condições de contorno, na solução de ótimo global para a Hanoi network, enquanto a aplicação do PSO na Balerma irrigation network demonstrou que o método proposto foi capaz de encontrar soluções quase ótimas para um sistema de grande porte com um tempo computacional razoável.   Palavras-chave: água, irrigação, análise econômica.     BEZERRA, S. T. M.; MACÊDO, J. E. S. LOOPED WATER DISTRIBUTION NETWORKS DESIGN VIA PARTICLE SWARM OPTIMIZATION ALGORITHM     2 ABSTRACT   This paper presents an optimization model for the design of pressurized water distribution systems for irrigation projects. The methodology is based on the Particle Swarm Optimization algorithm (PSO), which is inspired by the social foraging behavior of some animals such as flocking behavior of birds and the schooling behavior of fish. The proposed PSO has been tested on two benchmark problems reported in the literature, which correspond to the Hanoi network and an irrigation system located in Spain. The design resulted in the global optimum for the Hanoi network, while the application of PSO in Balerma irrigation network demonstrated that the proposed method was able to find almost optimal solutions for a large-scale network with reasonable computational time.   Keywords: water, irrigation, economic analysis. O desempenho do método foi comparado com trabalhos prévios, demonstrando convergência rápida e resultados satisfatórios na busca da solução ótima de um sistema com elevado exigência computacional.

scholarly journals Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ying-Yi Hong ◽  
Faa-Jeng Lin ◽  
Fu-Yuan Hsu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distribution System ◽  
Urban Areas ◽  
Distribution Systems ◽  
Particle Swarm ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Rural And Urban Areas ◽  
Enhanced Particle Swarm Optimization

The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV) arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW) may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

Cloud Adapt Particle Swarm Optimization Algorithm for Distribution Network Planning with Distributed Generation

2013 ◽  
Vol 427-429 ◽  
pp. 1136-1140 ◽  
Author(s):  
Bu Quan Xu ◽  
Li Chen Zhang ◽  
Bu Zhen Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Particle Swarm ◽  
Value Assessment ◽  
General Group ◽  
Swarm Optimization

Distributed Generation (DG) can be used to improve power quality, power supply reliability and reduce network loss et. Meanwhile Particle Swarm Optimization algorithm (PSO) is easy to fall into the local minimum. In this paper we propose a Cloud Adaptive Particle Swarm Optimization algorithm (CAPSO) to optimize the site and size of DG based on cloud model which has a tendency and randomness property. Judged by two dynamic value assessment, particle belongs to which group, excellent, general and poor. The inertia weight in general group is adaptively varied depending on X-conditional cloud generation. Taking the minimum network loss as the objective function, simulation on the IEEE 33BUS distribution systems to validate the methodology. Analysis and simulations indicate that it has good convergence speed and exactness.

scholarly journals Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ming-Yuan Cho ◽  
Thi Thom Hoang
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Binary Sequence ◽  
Particle Swarm ◽  
Time Domain Reflectometry ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Swarm Optimization

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

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