Feeder Load Balancing Using Genetic Algorithms and Artificial Neural Network

Authors

  • Aminu Adaira Suleiman Department of Electrical Engineering, Bayero University Kano, Nigeria
  • Kafayat Oluwatoyin Shobowale Faculty of Air Engineering, Air Force Institute of Technology Kaduna, Nigeria
  • Kumater Peter Ter Faculty of Air Engineering, Air Force Institute of Technology Kaduna, Nigeria
  • Yusuf Samuel Sunday Department of Electrical Engineering, Ahmadu Bello University Zaria, Nigeria
  • Razak Otokiti Department of Electrical Engineering, Federal University of Technology Minna, Nigeria
  • Mathias Usman Bonet Faculty of Air Engineering, Air Force Institute of Technology Kaduna, Nigeria
  • Aul Marcus Maafan Faculty of Air Engineering, Air Force Institute of Technology Kaduna, Nigeria

DOI:

https://doi.org/10.15282/mekatronika.v4i1.7494

Keywords:

Artificial Neural Network, Genetic Algorithms, Load Balancing, Network Reconfiguration, Power Distribution Feeder

Abstract

Low voltage power distribution system problems such as system planning, energy loss minimization and restoration usually involve proper load balancing or network reconfiguration procedures. To achieve an appreciable level of load phase balance, feeder reconfiguration using appropriate switching control strategy such as: Simulated Annealing, Tabu Search, Particle Swarm Optimization, and heuristic algorithms are viable preferences. However, the systematic solution to load phase balancing can be greatly enhanced optimally through implementation of an appropriate combinatorial optimization procedure such as Genetic Algorithms and Artificial Neural Network. Accordingly, this paper presents a genetic algorithms procedure to enhance the load phase balancing optimization and then train an artificial neural network to automate the reconfiguration of the distribution network loads, thus ensuring an optimal phase balancing in the system. An Intel® 2.0 GHz, 4GB RAM HP255 computer-based MATLAB® 14 was used for the neural network training, testing, and the implementation of the genetic algorithms. The outputs of the algorithms are the switching sequence for a balanced network. The parameters ΔIph (max - min) and Δ(Iph – Imax) which is the maximum difference between the phase currents, which are ideally zero if there are no imbalances in the network, shows considerable improvement in the balancing when compared with other literatures. This work presents the application examples of the proposed methods using real test data.

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Published

2022-06-15

How to Cite

[1]
A. A. . Suleiman, “Feeder Load Balancing Using Genetic Algorithms and Artificial Neural Network ”, MEKATRONIKA, vol. 4, no. 1, pp. 20–29, Jun. 2022.

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Original Article

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