Prediksi Pembebanan Yang Menyebabkan Tegangan Jatuh Dengan Menggunakan Metode Artificial Neural Network (ANN) Method

Saputra, Evanda Catur (2022) Prediksi Pembebanan Yang Menyebabkan Tegangan Jatuh Dengan Menggunakan Metode Artificial Neural Network (ANN) Method. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kestabilan tegangan menunjukkan kemampuan sistem dalam menjaga nilai tegangan di suatu bus pada kondisi normal maupun setelah terjadi gangguan. Apabila pada jaringan sistem tenaga listrik terjadi gangguan, maka akan menyebabkan penurunan tegangan secara bertahap dan tidak terkendali sehingga tegangan pada jaringan mengalami ketidakstabilan. Ketidakstabilan tegangan juga dapat disebabkan karena adanya ketidaksesuaian antara suplai dan permintaan daya reaktif, yaitu ketidakmampuan sistem untuk memenuhi kebutuhan daya reaktif. Analisis stabilitas tegangan diperlukan dalam perencanaan ataupun operasi sistem tenaga listrik. Apabila terdapat perubahan pembebanan pada suatu saluran, maka dapat menyebabkan menurunnya profil tegangan. Pada tugas akhir ini dilakukan prediksi nilai pembebanan yang menyebabkan tegangan jatuh dengan menggunakan metode Artificial Neural Network (ANN). setelah diperoleh hasil prediksi, selanjutnya adalah menganalisis stabilitas tegangan dengan menggunakan metode Fast Voltage Stability Index (FVSI). Dari hasil analisis didapatkan nilai FVSI tertinggi berada pada saluran dari bus 19 menuju bus 20 sebesar 0,96329. Hal tersebut terjadi saat beban reaktif pada bus 22 ditingkatkan menjadi 10,05832 MVAR.
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Voltage stability shows the ability of the system to maintain the voltage value on a bus under normal conditions and after a disturbance occurs. If there is a disturbance in the power system network, it will cause a gradual and uncontrolled voltage drop so that the voltage on the network experiences instability. Voltage instability can also be caused by a mismatch between supply and demand for reactive power, namely the inability of the system to meet reactive power requirements. Voltage stability analysis is needed in planning or operating an electric power system. If there is a change in loading on a line, it can cause a decrease in the voltage profile. In this final project, the prediction of the value of the load that causes the voltage drop is carried out using the Artificial Neural Network (ANN) method. After the prediction results are obtained, the next step is to analyze the voltage stability using the Fast Voltage Stability Index (FVSI) method. From the analysis results, the highest FVSI value is on the line from bus 19 to bus 20 of 0,96329. This happens when the reactive load on bus 22 is increased to 10,05832 MVAR.

Item Type:	Thesis (Other)
Additional Information:	RSE 621.319 Sap p-1 2022
Uncontrolled Keywords:	Kestabilan Tegangan, Perubahan Pembebanan, Tegangan jatuh, Artificial Neural Network (ANN), Fast Voltage Stability Index (FVSI). Voltage Stability, Load Change, Voltage Drop, Artificial Neural Network (ANN), Fast Voltage Stability Index (FVSI).
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	12 Jun 2026 02:15
Last Modified:	12 Jun 2026 02:15
URI:	http://repository.its.ac.id/id/eprint/133752

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