Paper Info
Title
Reconfiguration of Distribution Networks With Distributed Generations Using an Improved Neural Network Algorithm
Abstract
This study aims to suggest a new quasi-oppositional chaotic neural network algorithm (QOCNNA) for simultaneous network reconfiguration and distributed generations allocation (SNR-DG) in radial distribution networks (RDNs). The proposed QOCNNA is developed by combining original NNA with chaotic local search (CLS) and quasi-oppositional-based learning (QOBL) approaches. Integration of QOBL helps the algorithm improve exploration of the search space and convergence speed. Meanwhile, CLS can improve exploitation of the algorithm by performing local search around the current best solution. The objective of the SNR-DG problem is to define the network configuration, settings of distributed generations to optimize active power loss and voltage stability in RDNs. The performance of QOCNNA is tested on the SNR-DG problem with 33-, 69- and 118-bus RDNs considering different scenarios. The result analysis indicates that SNR-DG implementation is effective for power loss reduction and voltage stability improvement. Notably, QOCNNA yields active power loss reductions and voltage stability index improvements associated with case multi-objective SNR-DG at nominal load condition of {70.79%, 16.36%}, {84.02%, 8.59%}, and {57.79%, 10.10%}, respectively, for 33-, 69-, and 118-bus RDNs. In comparison with previous studies, QOCNNA is more effective in improving the performance of RDNs. Compared with other methods that we have applied, QOCNNA dominates the performance in solution accuracy, convergence speed, and robustness for all case studies. Also, QOCNNA finds effective and feasible solutions for daily variable load and generation scenario with the minimized total annual energy loss. Simulated outcomes in this scenario verify the superiority of QOCNNA over analytical-based approaches and the applied methods regarding total annual energy loss reduction and cost savings as well. There are hence simulation-based evidences to state that the CLS and QOBL help QOCNNA achieve a good trade-off between exploration and exploitation. Thus, QOCNNA has proved to be a favorable method in dealing with the SNR-DG problem.
Year
DOI
Venue
2021
10.1109/ACCESS.2021.3134872
IEEE ACCESS
Keywords
DocType
Volume
Voltage, Artificial neural networks, Stability criteria, Indexes, Metaheuristics, Resource management, Distributed power generation, Distributed generations, neural network algorithm, network reconfiguration, radial distribution networks
Journal
9
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Thanh Van Tran100.34
Bao-Huy Truong200.68
Tri Phuoc Nguyen372.26
Thi Anh Nguyen400.34
Thanh Long Duong500.34
Dieu Ngoc Vo601.01