To support production activities, Perum Peruri needs a stable electricity supply. A tap changer is used to keep the output voltage on the secondary side of the transformer constant with a value of 20 kV, which is installed on a 150 kV power transformer. The purpose of this study was to reduce the movement of taps on the OLTC to stabilize the secondary voltage of the 150 kV power transformer at the Perum Peruri substation. The data collection method used is a literature study to
obtain a theoretical basis, as well as using field studies with observation techniques to collect data in the form of secondary voltage (Vs), primary voltage (Vp), current, power, tap position, and power transformer specifications at the Perum Peruri
Substation.
The results showed that from 30 experiments, the largest regression value was obtained at 0.97629 with the smallest error value of 0.017% and the smallest MSE value of 0.02664 in experiment 8. Simulation with a regression value close to the number 1 and the smallest MSE value the result will be close to the tap value of the
OLTC target. Using a JST-based simulink diagram, the OLTC tap displacement on
February 08-10, 2022 was 23 times the OLTC tap displacement using AVR 39
times. This showed that the simulation of tap setting of the OLTC power transformer 150 kV Perum Peruri with artificial neural network-based simulinks performed was
successful.
Keywords: Transformer, Tap Changer, Artificial Neural Network, Matlab, Simulink, Backpropagation.