Fault section location for distribution network containing DG based on IBQPSO

2020 ◽  
Vol 20 (3) ◽  
pp. 937-949
Author(s):  
Min Zhao ◽  
Yan-Fang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulation Analysis ◽  
Distribution Network ◽  
Particle Swarm ◽  
Switching Function ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Smart Distribution Network ◽  
Global And Local ◽  
Processing Solution

To meet the requirement of the efficiency and accuracy for fault section location introduced by the construction and development of smart distribution network containing DG, a switching function model which can adapt itself to the change caused by switching of multiple DG is built and a network regional processing solution is raised. A fault section location for distribution network containing DG based on improved binary quantum particle swarm optimization (IBQPSO), which can effectively overcome the problem of global and local search capability imbalance in binary particle swarm optimization (BPSO), is proposed. The fault tolerance, rapidity and accuracy of this method are verified by simulation analysis of IEEE33 node system containing DG.

Rekonfigurasi Jaringan Menggunakan Binary Particle Swarm Optimization (BPSO) Pada Penyulang Suryagraha

2020 ◽  
Vol 1 (1) ◽  
pp. 22-30
Author(s):  
Diana Mulya Dewi ◽  
Nuzul Hikmah ◽  
Imam Marzuki ◽  
Ahmad Izzuddin
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electrical Network ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Good Efficiency ◽  
Voltage Loss

A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the endpoint. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses and improve distribution system reliability. Many feeders and buses on the network if calculated manually will be difficult and require a very long time. So it is necessary to solve problems using program assistance. In this case, use Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) algorithm based on the behavior of a herd of insects, such as ants, termites, bees, or birds. BPSO is a development of the PSO algorithm designed to solve the optimization problem in a discrete combination, where the particle takes the value of binary vectors with length n and speed which is defined as the probability of bits to reach value 1. The results show a significant reduction in losses.

scholarly journals Reconfiguration of Distribution Networks with Distributed Generation Using a Dual Hybrid Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Caoyuan Ma ◽  
Chunxiao Li ◽  
Xuezi Zhang ◽  
Guoxin Li ◽  
Yonggang Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Distribution Network ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Optimization Search

This paper proposes a reconfiguration strategy of distribution network with distribution generation (DG) based on dual hybrid particle swarm optimization algorithm. By the network structure simplification and branches grouping, network loss was selected as objective function, an improved binary particle swarm optimization algorithm (IBPSO) was used in branch group search, and the proposed group binary particle swarm optimization search algorithm was used in searching within the group to improve search efficiency and avoid early maturing. The proposed algorithm was tested on the IEEE 33-bus distribution power system and compared with other existing literature methods. The influence on the power flow of distribution network by DG position and capacity was studied. Simulation results illustrate that the proposed algorithm can get the optimal configuration results and significantly reduce system energy losses with fast convergence rate. In order to control the smart grid, using a dual hybrid particle swarm optimization algorithm to reconstruct a model, the result of simulation verifies the validity of the model. At the same time, the distributed power grid after reconstruction after optimization can effectively reduce the network loss and improve power supply quality.

Rekonfigurasi Jaringan Radial Distribusi Tenaga Listrik Penyulang Suryagraha Menggunakan Binary Particle Swarm Optimization (BPSO)

2019 ◽  
Vol 9 (2) ◽  
pp. 58-66
Author(s):  
Diana Mulya Dewi ◽  
Nuzul Hikmah ◽  
Imam Marzuki ◽  
Ahmad Izzuddin
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electrical Network ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Good Efficiency ◽  
Voltage Loss

A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the end point. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses and improve distribution system reliability. Many feeders and buses on the network if calculated manually will be difficult and require a very long time. So it is necessary to solve problems using program assistance. In this case, use Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) algorithm based on the behavior of a herd of insects, such as ants, termites, bees or birds. BPSO is a development of the PSO algorithm designed to solve the optimization problem in a discrete combination, where the particle takes the value of binary vectors with length n and speed which is defined as the probability of xn bits to reach value 1. The results show a significant reduction in losses .

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