Syamsudin, Nizar (2015) Minimisasi Rugi Daya Menggunakan Rekonfigurasi Jaringan Dan Penentuan Lokasi Distributed Generation (Dg) Pada Sistem Distribusi Dengan Metode Genetic Algorithm. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam perkembangan sistem tenaga listrik, sistem distribusi listrik
menjadi semakin luas dan kompleks sehingga menyebabkan rugi – rugi
yang terjadi pada sistem menjadi lebih besar. Untuk mengatasi hal
tersebut, cara yang umum dilakukan adalah dengan melakukan
rekonfigurasi jaringan dan memasang Distributed Generation (DG) pada
lokasi yang tepat. Genetic Algorithm (GA) merupakan salah satu metode
optimasi yang populer digunakan. GA menggabungkan secara acak
berbagai pilihan solusi terbaik di dalam suatu kumpulan (populasi)
untuk mendapatkan generasi solusi terbaik berikutnya (fitness). Proses
optimasi diujikan pada sistem IEEE 33 bus. Agar memperoleh hasil
yang optimal, proses optimasi dibagi menjadi empat kasus/skenario,
meliputi: sistem dasar, rekonfigurasi jaringan, penentuan lokasi DG serta
rekonfigurasi jaringan dan penentuan lokasi DG secara simultan. Kasus
pertama yang merupakan sistem dasar menghasilkan total rugi daya
sebesar 202,7 kW. Setelah dilakukan rekonfigurasi jaringan diperoleh
penurunan rugi daya sebesar 30,93%. Pada kasus ketiga, pemasangan
DG dilakukan berdasarkan jumlah DG yang dipasang, yaitu satu unit
DG hingga tiga unit DG. Hasil penempatan DG mulai dari satu hingga
tiga unit DG diperoleh penurunan rugi daya masing – masing sebesar
36,24894%, 56,77889% dan 64,31672%. Sedangkan dengan melakukan
rekonfigurasi jaringan dan penempatan DG secara simultan diperoleh
penurunan rugi daya sebesar 68,3371%.
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In the development of electric power systems, electrical
distribution systems become increasingly extensive and complex,
causing losses that occur in the system becomes larger. To overcome
this, the common way is to do with the reconfiguration of the network
and install Distributed Generation (DG) in the appropriate location.
Genetic Algorithm (GA) is one of the popular methods of optimization
used. GA combines a random variety of the best solution in a set
(population) to get the next generation of the best solution (fitness). The
optimization process was tested on the IEEE 33 bus system. In order to
obtain optimal results, the optimization process is divided into four cases
/ scenarios, including: a base system, reconfiguration of the network,
determining the location of the DG as well as reconfiguration of the
network and determining the location DG simultaneously. The first case
that is a base system generates power losses amounted to 202.7 kW.
After the reconfiguration of the network obtained decrease power losses
amounted to 30.93%. In the third case, the installation is done based on
the number of DG mounted, ie one unit of DG to three units. DG
placement results ranging from one to three units of DG obtained
decrease power loss each amount to 36.24894% 56.77889% and
64.31672%. While the conduct of network reconfiguration and
placement DG simultaneously obtained decrease power losses amounted
to 68.3371%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSE 621.319 Sya m |
| Uncontrolled Keywords: | rekonfigurasi jaringan, penempatan DG, sistem distribusi, genetic algorithm ================================================================== network reconfiguration, DG placement, distribution system, genetic algorithm |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK201 Electric Power Transmission T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | - Taufiq Rahmanu |
| Date Deposited: | 27 Mar 2019 03:53 |
| Last Modified: | 27 Mar 2019 08:10 |
| URI: | http://repository.its.ac.id/id/eprint/62598 |
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