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Susatyo Handoko
Department of Electrical Engineering, Universitas Diponegoro

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Perbaikan Rugi-Rugi Daya Listrik Menggunakan Kapasitor Bank dan Tap Pengubah Sadapan Dengan Algoritma Shark Smell Radiktyo Nindyo Sumarno; Susatyo Handoko; Mochammad Facta
TEKNIK Vol 41, No 3 (2020)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v41i3.24818

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.