Analisis Peningkatan Kualitas DVR Menggunakan Metode PSO dan ANN terhadap Kedip Tegangan (Studi Kasus: PT. PLN (Persero) UP3 Sibolga Penyulang SB02)

Abstract

Power Quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic Voltage Restorer (DVR) is an efficient and flexible solution in overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder's length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with Matlab – Simulink. The simulation results show that voltage sag is successfully recovered by DVR-PSO and DVR-ANN by supplying voltage at each phase. Based on the results of the analysis, it shows that DVR ANN is better than DVR-PSO in injecting voltage into the network.