DOI: https://doi.org/10.14710/teknik.v41i3.24818

The Improvement of Electric Power Losses Using Bank Capacitor and Tap Changer With Shark Smell Algorithm

Perbaikan Rugi-Rugi Daya Listrik Menggunakan Kapasitor Bank dan Tap Pengubah Sadapan Dengan Algoritma Shark Smell

*Radiktyo Nindyo Sumarno  -  Master Program of Electrical Engineering, Universitas Diponegoro, Indonesia
Susatyo Handoko  -  Department of Electrical Engineering, Universitas Diponegoro, Indonesia
Mochammad Facta  -  Department of Electrical Engineering, Universitas Diponegoro, Indonesia
Open Access Copyright (c) 2020 TEKNIK

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Abstract
One way to optimize the transmission line is to reduce electrical power losses. Tap changers on power transformers and bank capacitors can be used to regulate the system voltage resulting in lower power losses in the transmission line. Determining the value of tap settings and bank capacitors in the planning process is challenging to do with certainty. It is generally carried out through a trial and error mechanism using the power flow method. Since the determination of tap settings and bank capacitors values is difficult to do with certainty, this research was carried out with optimization with the shark smell algorithm. Such optimization aims to get a more appropriate tap changer and capacitor bank change values on the IEEE 30-bus system. In this study, several optimizations were carried out, namely optimization of tap settings, optimization of bank capacitors, and tap setting optimization combined with bank capacitors' optimization. Conducting tap setting optimization, we obtained an active power loss of 0.65% from the condition without optimization. In optimizing bank capacitors, we reduce active power losses of 0.90% compared to conditions without optimization. In optimizing the combination of tap setting and bank capacitors, the active power losses are reduced by 1.23%. Comparing the results of all these optimizations shows that the combination of bank tap setting and capacitor optimization is obtained by reducing the most active power losses. In this study, the reduction of active power losses resulted in 217.2 kW. The results show that the Shark Smell algorithm can provide better optimization results of 1.23% compared to conditions without optimization based on the test value.
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Keywords: bank capacitors; tap changer; electric power losses; Shark Smell algorithm

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  4. Alamajibuwono, H., Sukmadi, T., & Handoko, S. (2011). Optimasi Penempatan Kapasitor Menggunakan Algoritma Genetika Pada Sistem Distribusi Untuk Memperbaiki Faktor Daya dan Tegangan. Disertasi. Universitas Diponegoro
  5. Aryanezhad, M. (2018). Management and coordination of LTC,SVR,shunt capacitor and energy storage with high PV penetration in power distribution system for voltage regulation and power loss minimization. Electrical Power and Energy Systems, 100, 178–192
  6. Bangun, A. W. (2016). Penempatan SVC Untuk Memperbaiki Profil Tegangan Pada Jaringan Transmisi PT. PLN Lampung. Universitas Lampung
  7. Ehteram, M., Karami, H., M., S.-F., El-Shafie, A., & Amini, Z. (2017). Optimizing Dam and Reservoirs Operation Based Model Utilizing Shark Algorithm Approach. Knowledge-Based System, 122, 26–38
  8. Ganguly, S., & Samajpati, D. (2017). Distributed Generation Allocation With On-Load Tap Changer On Radial Distribution Network Using Adaptive Genetic Algorithm. Applied Soft Computing, 59, 45–67
  9. Gnanasekaran, N., Chandramohan, S., Kumar, P. S., & Mohamed Imran, A. (2016). Optimal placement of capacitors in radial distribution system using shark smell optimization algorithm. Ain Shams Engineering Journal, 7(2), 907–916. https://doi.org/10.1016/j.asej.2016.01.006
  10. Gomes, M. H., & Saraiva, J. T. (2009). A market based active/reactive dispatch including transformer taps and reactor and capacitor banks using Simulated Annealing. Electric Power Systems Research, 79, 959–972
  11. Hosseinpour, H., & Bastaee, B. (2015). Optimal placement of on-load tap changers in distribution networks using SA-TLBO method. International Journal of Electrical Power and Energy Systems, 64, 1119–1128. https://doi.org/10.1016/j.ijepes.2014.09.009
  12. Saadat, H. (1999). Power System Analysis. New York: The McGraw-Hill
  13. Singh, S. P., & Rao, A. R. (2012). Optimal allocation of capacitors in distribution systems using particle swarm optimization. International Journal of Electrical Power and Energy Systems, 43(1), 1267–1275. https://doi.org/10.1016/j.ijepes.2012.06.059
  14. Szuvovivski, I., Fernandes, T. S. P., & Aoki, A. R. (2012). Simulation allocation of capacitors and voltage regulators at distribution networks using Genetic Algorithms and Optimal Power Flow. International Journal of Electrical Power and Energy Systems, 40(1), 62–69. https://doi.org/10.1016/j.ijepes.2012.02.006
  15. Tampubolon, D., & Sjani, M. (2014). Optimalisasi Penggunaan Kapasitor Bank Pada Jaringan 20kV Dengan Simulasi ETAP (Studi Kasus Pada Feeder Srikandi di PLN Rayon Pangkalan Balai, Wilayah Sumatera Selatan). Diakses dari https://jurnal.usu.ac.id/index.php/singuda_ensikom/article/download/7689/4014
  16. Vuletić, J., & Todorovski, M. (2014). Optimal capacitor placement in radial distribution systems using clustering based optimization. Electrical Power and Energy Systems ScienceDirect, 62, 229–236
  17. Ziari, I., Ledwich, G., & Ghosh, A. (2013). A new technique for optimal allocation and sizing of capacitors and setting of LTC. International Journal of Electrical Power and Energy Systems, 46(1), 250–257. https://doi.org/10.1016/j.ijepes.2012.09.010

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Last update: 2025-07-26 10:32:08

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