Tauladan, Imam Suri (2021) Setting Directional Overcurrent Relay Adaptif pada Sistem Closed Loop Ring Bus dengan Skema Generator dan Feeder State. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penelitian ini dilakukan pada sistem kelistrikan industri petrochemical yang saling terintegrasi satu sama lainnya. Sistem kelistrikan menggunakan konfigurasi closed loop ring bus dimana beban disuplai dari masing-masing feeder yang saling terintegrasi. Skema on/off generator pada sistem menyebabkan perubahan nilai arus hubung singkat jika terjadinya gangguan. Tentu hal tersebut berdampak pada setting koordinasi DOCR yang selalu berubah-ubah. Selain skema operasi pembangkit, hal yang harus diperhatikan adanya kemungkinan perubahan kondisi feeder yang menyebabkan aliran arus hubung singkat berubah. Untuk itu perlu adanya solusi untuk mengatasi permasalahan tersebut, sehingga dalam penelitian ini melakukan setting koordinasi DOCR yang optimal dan adaptif dengan menggunakan Algoritma Differential Evolution (DE) dan Artificial Neural Network (ANN). Algoritma DE bertujuan untuk memperoleh setting yang optimal dan ANN Backpropagation Levenberg-Marquardt bertujuan untuk setting yang adaptif.
Pada penelitian ini mengimplementasikan beberapa strategi mutasi (DE/best/1 dan DE/rand-to-best/1) untuk mendapatkan Time Dial Setting (TDS) yang optimal. Hasil optimasi TDS digunakan sebagai data target pada proses pelatihan ANN. Selanjutnya dilakukan proses pengujian untuk mendapatkan setting TDS yang adaptif berdasarkan skema perubahan kondisi. Hasil dari penelitian ini menunjukkan bahwa dengan menggunakan DE-ANN menghasilkan setting DOCR yang optimal dan adaptif.
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This research was conducted on the integrated electrical system of the petrochemical industry. The electrical system uses a closed-loop ring bus configuration where the load was supplied by interconnected feeder system. When one generator is under normal shutdown operation or by terms ‘generator scheme’, the short circuit values are change. It has an impact on the DOCRs setting which is always changing. In addition to the generator operating scheme, notice that the possibility of changes in feeder will cause short-circuit currents to change. For this reason, it is necessary to find a solution to overcome these problems, so that in this study, optimal and adaptive DOCR coordination settings are implemented using the Differential Evolution (DE) Algorithm and Artificial Neural Network (ANN). DEA method is used to obtain the optimal Time Dial Setting (TDS) and Backpropagation Levenberg-Marquardt Neural Network is used to obtain the adaptive setting DOCRs.
This research implements several mutation strategies (DE/best/1 and DE/rand-to-best/1) of DE algorithm to get the optimal TDS. The result concludes that DE/best/1 is more optimal than DE/rand-to-best/1. The optimal result will be used as the target data in ANN training process. Furthermore, the testing process is implemented to obtain an adaptive TDS setting by considering generator and feeder state schemes. This study concludes that DE-ANN obtains the optimal and adaptive DOCRs setting.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Artificial Neural Network (ANN), Differential Evolution (DE), Time Dial Setting (TDS), Skema on/off generator, Skema feeder state. Artificial Neural Network (ANN), Differential Evolution (DE), Time Dial Setting (TDS), On/off generator scheme, Feeder state scheme. |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3030 Electric power distribution systems |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Imam Suri Tauladan |
| Date Deposited: | 10 Aug 2021 12:05 |
| Last Modified: | 12 Aug 2021 09:41 |
| URI: | http://repository.its.ac.id/id/eprint/85662 |
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