scholarly journals Optimasi Penempatan dan Kapasitas Bank Kapasitor Untuk Mereduksi Rugi-Rugi Daya Menggunakan Kombinasi Metode Loss Sensitivity Factors dan Particle Swarm Optimization (PSO)

Electrician ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 48
Author(s):  
Fikri Adi Anggara ◽  
Osea Zebua ◽  
Khairuddin Hasan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Sensitivity Factors ◽  
Active Power Losses

Intisari — Sistem distribusi yang memiliki saluran panjang biasanya memiliki masalah rugi-rugi daya dan votage drop. Masalah ini dapat diatasi dengan cara menempatkan bank kapasitor pada sistem distribusi. Masalah utama dalam penempatan bank kapasitor adalah menentukan lokasi dan kapasitas yang optimal. Penelitian ini menggunakan metode Loss Sensitivity Factors (LSF) untuk menentukan kandidat bus yang akan dipilih sebagai lokasi penempatan bank kapasitor dan metode algoritma Particle Swarm Optimization digunakan untuk mencari kapasitas optimal bank kapasitor yang dipasang. Simulasi dilakukan dengan menggunakan perangkat lunak MATLAB. Studi kasus yang digunakan adalah kasus uji IEEE 10 bus dan penyulang Pakis di gardu induk Menggala. Hasil simulasi menunjukkan bahwa kombinasi kedua metode dapat menentukan lokasi penempatan dan kapasitas optimal bank kapasitor dengan reduksi rugi-rugi daya yang minimum untuk kedua studi kasus.Kata kunci — Rugi-Rugi Daya, Bank Kapasitor, Loss Sensitivity Factors, Algoritma Particle Swarm Optimization. Abstract — Distribution systems that have long lines usually have problems with power losses and votage drop. This problem can be overcome by placing a bank capacitor in the distribution system. The main problem in the placement of bank capacitors is determining the optimal location and capacity. This study uses the Loss Sensitivity Factors (LSF) method to determine the candidate bus that will be selected as the location of the placement of bank capacitors and the Particle Swarm Optimization algorithm method is used to find the optimal capacity of the installed bank capacitor. Simulation is done using MATLAB software. The case studies used were the IEEE 10 bus and Pakter feeder test cases at the Menggala substation. The simulation results show that the combination of the two methods can determine the placement location and optimal capacitor capacity of the bank by reducing the minimum power losses for the two case studies.Keywords— Active power losses, Capacitor Bank, Loss Sensitivity Factors, Particle Swarm Optimization Algorithm.

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.

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.

Impacts of Distributed Generators on Voltage Sag Analysis

2013 ◽  
Vol 732-733 ◽  
pp. 877-881
Author(s):  
Jenjira Boonnamol ◽  
Thavatchai Tayjasanant
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Test System ◽  
Voltage Sag ◽  
Swarm Optimization ◽  
Distributed Generators ◽  
Three Phase

This paper presents impacts of distributed generators (DGs) such as synchronous-based DG and inverter-based DG on voltage sag analysis in distribution systems. Voltage sag analysis is assessed through area of vulnerability (AOV), number of sags frequency (NSF) and voltage sag index (SARFI). Single line-to-ground and three-phase faults are investigated. Size and location of DG are carried out by using Particle Swarm Optimization algorithm (PSO) in order to minimize losses and number of sag frequency. Roy Billinton Test System (RBTS) Bus 2 is used for simulation cases. Results show that the distribution system with DG installed improves voltage sag performance compared with the system without DG installed.

Sizing and Locating of Distributed Generations Based on Chaos Particle Swarm Optimization Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2119-2123
Author(s):  
Ying Chen ◽  
Lian Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Particle Swarm Optimization Algorithm ◽  
Operating Cost ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Distributed Generations ◽  
Minimum Operating

Optimal configuration of distributed generations (DGs) is an important topic in the developing of intelligent power grid. In this paper, to finding out the optimal sitting and sizing Of DGs in distribution network without considering the newly increased load nodes, an economical model is built that regards the annual minimum operating cost as the objective function. Meanwhile, in order to avoid the particles trapping in local optimum, the chaos particle swarm optimization algorithm (CPSO) is adopted. Through testing on the IEEE 33-bus radial distribution system, the analysis results showed that the algorithm is effective.

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