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Journal : Jurnal JEETech

Rekonfigurasi Jaringan Distribusi Radial 65 Bus Berbasis Binary Particle Swarm Optimization (BPSO) Ali, Machrus; Nurohmah, Hidayatul; Ajiatmo, Dwi
Jurnal JEETech Vol. 3 No. 1 (2022): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.52 KB) | DOI: 10.32492/jeetech.v3i1.3108

Abstract

The configuration of a radial distribution network is difficult to simplify because it is very complex. This network reconfiguration is used to redesign the configuration of the radial distribution network by opening and closing switches on the distribution network. The feeder of Purwoasri, The feeder of Purwoasri, Rayon Kertosono has 65 buses which cause the Mojokerto area to have a very large loss so it needs to be reconfigured.. The resulting power flow will result in network power losses due to configuration. The reconfiguration process will be repeated until a configuration form that produces the smallest power losses is obtained. The number of feeders and buses on the network will be difficult if done manually and takes a very long time, so solving the problem must use a computer program. Network reconfiguration using the Matlab 2013a program will analyze its power flow using the Newton Raphson method and using the Binary Particle Swarm Optimization (BPSO) artificial intelligence method. Before reconfiguration, the network experienced losses of 1169,1374 kWatt after reconfiguration experienced losses of 635,7444 kWatt. The results of the reconfiguration can reduce losses of 635,74440 kWatt or 45,6228 % from the previous loss.
Komparasi PID, FLC, dan ANFIS sebagai Kontroller Dual Axis Tracking Photovoltaic berbasis Bat Algorithm Nurohmah, Hidayatul; Ali, Machrus; Ajiatmo, Dwi
Jurnal JEETech Vol. 3 No. 2 (2022): Nomor 2 November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.001 KB)

Abstract

Photovoltaic is a renewable electrical energy generator that is very suitable for tropical countries that get a lot of sunlight. However, this generator has low efficiency. To overcome this deficiency, several researchers have optimized the conventional dual-axis tracking solar method. Research is needed to optimize using artificial intelligence, in this case, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Bat Algorithm (BA). By comparing the performance of the model without control, conventional PID model, PID Auto tuning MatLab, Fuzzy Logic Controller (FLC) method, ANFIS method, and ANFIS-BA method. The simulation results show that the best model design on the horizontal axis and vertical axis dual tracking photovoltaic is ANFIS-BA with the smallest overshot, smallest undershot, and the fastest settling time of all model designs.