Perencanaan Metode Penyeimbangan Beban Pada Jaringan Distribusi Sekunder Menggunakan Artificial Intelligence Berbasis Logika Fuzzy
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Prananda, Rivzky
Satya Dini, Hasna
Oktaviana Putri, Tri Wahyu
Satya Dini, Hasna
Oktaviana Putri, Tri Wahyu
Subject
Teknik Elektro
Datestamp
2022-09-28 08:05:40
Abstract :
Load imbalance is a condition when the load currents from the R, S, and T phases are uneven. Load imbalance can reduce the efficiency of the transformer. According to the Circular Letter of the Board of Directors of PT. PLN (Persero) No. 0017E/DIR/2014 concerning Distribution Transformer Maintenance Methods stipulates a good load imbalance, which is below 10%. Based on the results of load measurements carried out on the distribution transformer NBR 022 with a capacity of 200 kVA supplied by the Matoa feeder, it was found that the load imbalance value was 11.25% in 2021 semester 1 and 12.74% in 2019 semester 1. The method used in this study is a quantitative approach with experimental methods. Data from measurements at night when peak load times in 2019 and 2020 and semester 1 of 2021 are used as data for load balancing using the fuzzy logic method. The results after using fuzzy logic to overcome load imbalances are 4.18% in 2021 semester 1 and 8.29% in 2019 semester 1. These results show a decrease in load imbalance below 10%. In addition, the percentage of loading in each phase is more evenly distributed after using fuzzy logic. This means that the fuzzy logic program is able to work well for load balancing.
Load imbalance is a condition when the load currents from the R, S, and T phases are uneven. Load imbalance can reduce the efficiency of the transformer. According to the Circular Letter of the Board of Directors of PT. PLN (Persero) No. 0017E/DIR/2014 concerning Distribution Transformer Maintenance Methods stipulates a good load imbalance, which is below 10%. Based on the results of load measurements carried out on the distribution transformer NBR 022 with a capacity of 200 kVA supplied by the Matoa feeder, it was found that the load imbalance value was 11.25% in 2021 semester 1 and 12.74% in 2019 semester 1. The method used in this study is a quantitative approach with experimental methods. Data from measurements at night when peak load times in 2019 and 2020 and semester 1 of 2021 are used as data for load balancing using the fuzzy logic method. The results after using fuzzy logic to overcome load imbalances are 4.18% in 2021 semester 1 and 8.29% in 2019 semester 1. These results show a decrease in load imbalance below 10%. In addition, the percentage of loading in each phase is more evenly distributed after using fuzzy logic. This means that the fuzzy logic program is able to work well for load balancing.
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