Quantum binary particle swarm optimization for optimal on-load tap changing and power loss reduction

Aji Akbar Firdaus, Irrine Budi Sulistiawati, Vicky Andria Kusuma, Dimas Fajar Uman Putra, Hamzah Arof, Novian Patria Uman Putra, Sena Sukmananda Suprapto

Research output: Contribution to journalArticlepeer-review


Over time, there has been a continuous surge in the demand for electrical energy, necessitating the development of larger and more intricate electrical power networks. These extensive networks pose a significant challenge, primarily in the form of considerable loss of electrical energy, which, if not effectively addressed, may lead to persistent and imperceptible losses. In response to this challenge, this research proposes the application of quantum binary particle swarm optimization (QBPSO) for the coordinated management of on-load tap changers (OLTC) in loaded transformers within a distribution network, with a specific emphasis on reducing power losses. The experimental results demonstrate that the implementation of QBPSO results in a reduction of power loss from 21.756107 kW to 19.157321 kW and an increase in the average voltage from 19.00467941 kV to 19.93068 kV in a 20 kV 34-bus distribution network. This has the potential to significantly enhance overall system efficiency.

Original languageEnglish
Pages (from-to)488-498
Number of pages11
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number2
Publication statusPublished - Apr 2024


  • On-load tap changers
  • Power loss
  • Quantum binary particle swarm optimization
  • Transformer
  • Voltage drop


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