Minimisasi Rugi Daya Menggunakan Rekonfigurasi Jaringan Dan Penentuan Lokasi Distributed Generation (Dg) Pada Sistem Distribusi Dengan Metode Genetic Algorithm

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%. ======================================================================== 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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