Optimal Tuning of OLTC to Improve Power Transformer Voltage Stability based on Artificial Neural Network (Case Study in PT. YTL East Java)
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Research Paper | Electrical Engineering | Indonesia | Volume 6 Issue 10, October 2017

Optimal Tuning of OLTC to Improve Power Transformer Voltage Stability based on Artificial Neural Network (Case Study in PT. YTL East Java)

Azmi Saleh, Widyono Hadi, Welli Agustina

This paper proposed an artificial neural network (ANN) based on Levenberg-Marquardt algorithm which attempts to improve the voltage stability of the power transformer with respect to minimum real power loss and proper voltages profile. This algorithm uses optimum settings of On-Line Tap Changer (OLTC) transformer and a minimum number of Reactive Power Compensation Equipment (RPCE). The proposed algorithm is programmed on MATLAB and examine on power transformer 500 kV in PT YTL East Java to demonstrate the validity and the convenience of back propagation (BP) approach with promising results. The results show that the proposed BP algorithm is reducing the position changes as much as 36 times to 14 times in hours comparing using controlled by the automatic voltage control (AVC). Also, the secondary voltage of power transformers is more stable.

Keywords: on-load-tap changer OLTC, ANN, voltage stability, reactive power

Edition: Volume 6 Issue 10, October 2017

Pages: 1422 - 1426

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How to Cite this Article?

Azmi Saleh, Widyono Hadi, Welli Agustina, "Optimal Tuning of OLTC to Improve Power Transformer Voltage Stability based on Artificial Neural Network (Case Study in PT. YTL East Java)", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=11091701, Volume 6 Issue 10, October 2017, 1422 - 1426

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