Distribution network reconfiguration based on artificial network reconfiguration for variable load profile

Youssef, Hesham Hanie and Mokhlis, Hazlie and Abu Talip, Mohamad Sofian and Al Samman, Mohammad and Muhammad, Munir Azam and Mansor, Nurulafiqah Nadzirah (2020) Distribution network reconfiguration based on artificial network reconfiguration for variable load profile. Turkish Journal of Electrical Engineering and Computer Sciences, 28 (5). pp. 3013-3035. ISSN 1300-0632, DOI https://doi.org/10.3906/ELK-1912-89.

Full text not available from this repository.
Official URL: https://doi.org/10.3906/ELK-1912-89


Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. The proposed ANN model is tested on IEEE 33-bus, IEEE 69-bus, and IEEE-118 bus systems. The test results indicate the efficiency of the proposed technique in three aspects: processing time, simple structure, and high accuracy. © TÜBİTAK

Item Type: Article
Funders: University of Malaya and Malaysian government for research grants (Grant code: GPF016A-2019)
Uncontrolled Keywords: Artificial neural networks; Distribution system; Evolutionary programming; Network reconfiguration
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 10 Feb 2021 01:03
Last Modified: 10 Feb 2021 01:03
URI: http://eprints.um.edu.my/id/eprint/25744

Actions (login required)

View Item View Item