Reconfiguration of Distribution Network with Distributed Energy Resources Integration Using PSO Algorithm

Syahputra, Ramadoni; Robandi, Imam; Ashari, Mochamad

doi:10.12928/telkomnika.v13i3.1790

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…An individual represents the probable solution and every group of individuals represents a swarm. This theory was first put forward in 1995 [26]. Each individual has best position discover by the individual it self and it is stored in a memory called local best position (P best ), and the best position discovered among all individuals (P best ) in the swarm also stored in a memory called global best position (G best ), at every step the location of P best and G best are changing.…”

Section: Optimization Process 31 Simple Pso Algorithmmentioning

confidence: 99%

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

Shri

Mijbas

2021

IJEECS

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In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node -118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/m-file). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (P loss), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.

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Section: Optimization Process 31 Simple Pso Algorithmmentioning

confidence: 99%

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

Shri

Mijbas

2021

IJEECS

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“…The substation is a part of a power system which has main component that is power transformer. Transformer serves to transform electrical power by changing the system voltage [19][20][21][22][23]. The capacity of substation power transformer needs to be considered, it is intended to be aware of the range of power transformer's ability to meet the needs of consumer electricity.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation

Syahfitra

Syahputra

Putra

2017

jet

Self Cite

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The National Electricity Company (PT PLN) should have an estimated peak load of the substation transformer in the future. This is useful to be able to achieve transformer capability and can be used as a first step to anticipate the possibility of replacement of a new transformer. This research presents a peak load forecasting system transformer1 and transformer2 in Bumiayu substation using Backpropagation Artificial Neural Network (ANN). This study includes the procedures for establishing a network model and manufacture forecasting system based GUI (Graphic User Interface) using MATLAB 2015a. The formation of the network model refers to input variables consisting of GRDP data, population data and historical data of peak load of transformer. In this research, a multilayer network model, which consists of 1 input layer, 2 hidden layers and 1 output layer, is used. The peak load forecasting of transformer1 produces 5.7593e-08 for training MSE and 5.3784e-04 for testing MSE. Meanwhile, forecasting the peak load transformer2 generated 3.3433e-08 for training MSE and 9,4710e-04 for testing MSE.

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“…Since PSO was introduced, it has become a popular optimizer and has widely been applied in practical problems. Syahputra [3] uses PSO Algorithm to optimize configuration of distribution network with distributed energy resources integration. Lu [4] introduces PSO algorithm into the Artificial Neural Network training and construct a BP neural network model to improve the prediction accuracy for the cost forecasting of transmission line project based on historical project data.…”

Section: Introductionmentioning

confidence: 99%

A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients

Gong

et al. 2016

Bulletin EEI

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Particle swarm optimization (PSO) is one of the famous heuristic methods. However, this method may suffer to trap at local minima especially for multimodal problem. This paper proposes a modified particle swarm optimization with dynamic acceleration coefficients (ACPSO). To efficiently control the local search and convergence to the global optimum solution, dynamic acceleration coefficients are introduced to PSO. To improve the solution quality and robustness of PSO algorithm, a new best mutation method is proposed to enhance the diversity of particle swarm and avoid premature convergence. The effectiveness of ACPSO algorithm is tested on different benchmarks. Simulation results found that the proposed ACPSO algorithm has good solution quality and more robust than other methods reported in previous work.

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Reconfiguration of Distribution Network with Distributed Energy Resources Integration Using PSO Algorithm

Abstract: This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using modified particle swarm optimization (PSO)

Cited by 19 publications

References 22 publications

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation

A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients

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