scholarly journals A new hybrid algorithm for solving distribution network reconfiguration under different load conditions

2020 ◽  
Vol 20 (3) ◽  
pp. 1118
Author(s):  
Omar Muhammed Neda
Keyword(s):  
Distribution Network ◽  
Optimal Solution ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Local Optima ◽  
Distribution Network Reconfiguration ◽  
Node Voltage

<p>Distribution Network Reconfiguration (DNR) is a significant problem for<br />keeping the network running under normal conditions. In this study, for<br />preventing the premature convergence issue, also to improving the searching<br />ability of the Binary Particle Swarm Optimization (BPSO) algorithm, chaotic<br />strategy is incorporating with BPSO algorithm to create a new hybrid<br />algorithm called Chaotic BPSO (CBPSO). Undeniably, the chaotic strategy<br />enables the hybrid CBPSO algorithm to slip from the local optima and also to<br />reach optimal solution in fewer number of iterations compare to BPSO due to<br />the remarkable behavior and ergodic of the chaos strategy than random<br />search in BPSO algorithm. The CBPSO algorithm is presented as a<br />advantageous optimization tool for solving DNR. In this problem, decreasing<br />of real power loss () is an objective function while node voltage, system<br />radially and line current have been utilized as a constrains of the system. The<br />search space in this problem for the presented technique is a group of lines<br />(switches) that are normally opened or closed. Two types of loads are<br />presented: the constant and variable loads for testing the efficacy of the<br />CBPSO method for tackling DNR problem when the load is changes. The<br />proposed technique is implemented on IEEE Node system by utilizing<br />R2013b software for verifying the efficacy of CBPSO technique. The<br />simulation results confirm that technique has high ability in reducing and<br />raising the voltage profile of the grid compared to and other procedures in<br />the literature.</p>

scholarly journals A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sidun Fang ◽  
Xiaochen Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Hybrid Algorithm ◽  
Distribution Network ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Network Reconfiguration ◽  
Swarm Optimization ◽  
Distribution Network Reconfiguration ◽  
Locality Property

This paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability to avoid local optimal solution. After a thorough comparison, network random key (NRK) is introduced as the corresponding coding strategy among various tree representation strategies. NRK could completely avoid the generation of infeasible solutions during the searching process and has a good locality property, which allows the new hybrid algorithm to perform to its fullest potential. The proposed algorithm has been validated through an IEEE 33 bus test case. Compared with other algorithms, the proposed method is both accurate and computationally efficient. Furthermore, a test to solve another problem also proves the robustness of the proposed algorithm for a different problem.

scholarly journals Multi-Objective Power Distribution Network Reconfiguration using Chaotic Fractional Particle Swarm Optimization

2021 ◽  
Vol 19 (1) ◽  
pp. 43-50
Author(s):  
Yashar Mousavi ◽  
Mohammad Hosein Atazadegan ◽  
Arash Mousavi
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Particle Swarm ◽  
Superior Performance ◽  
Network Reconfiguration ◽  
Power Distribution Network ◽  
Swarm Optimization ◽  
Distribution Network Reconfiguration ◽  
Multi Objective

Optimization of power distribution system reconfiguration is addressed as a multi-objective problem, which considers the system losses along with other objectives, and provides a viable solution for improvement of technical and economic aspects of distribution systems. A multi-objective chaotic fractional particle swarm optimization customized for power distribution network reconfiguration has been applied to reduce active power loss, improve the voltage profile, and increase the load balance in the system through deterministic and stochastic structures. In order to consider the prediction error of active and reactive loads in the network, it is assumed that the load behaviour follows the normal distribution function. An attempt is made to consider the load forecasting error on the network to reach the optimal point for the network in accordance with the reality. The efficiency and feasibility of the proposed method is studied through standard IEEE 33-bus and 69-bus systems. In comparison with other methods, the proposed method demonstrated superior performance by reducing the voltage deviation and power losses. It also achieved better load balancing.

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

2014 ◽  
Vol 699 ◽  
pp. 809-815 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
Mohd Hafiz Jali ◽  
Wan Mohd Bukhari ◽  
M.N.M. Nasir ◽  
Hazriq Izzuan Jaafar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Time ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Swarm Optimization

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

scholarly journals Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1544 ◽  
Author(s):  
Damir Jakus ◽  
Rade Čađenović ◽  
Josip Vasilj ◽  
Petar Sarajčev
Keyword(s):  
Distribution Network ◽  
Distribution Networks ◽  
Total Power ◽  
Computational Time ◽  
Small Scale ◽  
Network Reconfiguration ◽  
Optimal Distribution ◽  
Voltage Profile ◽  
Distribution Network Reconfiguration ◽  
Optimal Reconfiguration

This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

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