Penambahan Gardu Distribusi Berdasarkan Pertumbuhan Beban Listrik Menggunakan GUI Matlab di Wilayah Tangerang

Penulis

  • Adri Senen Institut Teknologi PLN
  • Oktaria Institut Teknologi PLN
  • Christine Widyastuti Institut Teknologi PLN

DOI:

https://doi.org/10.55893/jt.vol22no1.499

Kata Kunci:

pertumbuhan beban, gardu distribusi, transformator, graphic user interface

Abstrak

Perencanaan pengembangan sistem distribusi menjadi suatu yang sangat penting seiring dengan peningkatan kebutuhan beban listrik, dengan tetap memperhatikan sisi efisiensi penyaluran dan kualitas daya yang disalurkan ke konsumen. Penambahan jaringan distribusi tentunya akan mengakibatkan penambahan kapasitas dan jumlah transformator serta gardu distribusi. Oleh karena itu diperlukan perhitungan yang tepat untuk penentuan hal tersebut. Dalam penelitian ini, penambahan gardu distribusi didasarkan pada pemilihan rating trafo distribusi berdasarkan pertumbuhan bebannya. Pembebanan trafo distribusi dibuat maksimum 80 % dari rating kapasitasnya dengan model terdistribusi merata. Perhitungan penambahan Transformator distribusi memerlukan suatu pendekatan untuk menghubungkan total Transformator distribusi dan total gardu distribusi yaitu hasil rata-rata dari total Transformator distribusi dibagi dengan total gardu distribusi sehingga didapat nilai penambahan Gardu distribusi. Untuk merencanakan penambahan gardu distribusi memerlukan perhitungan yang cukup kompleks dan rumit. Agar perencanaan penambahan gardu distribusi menjadi lebih mudah, maka dapat menggunakan Graphical User Interface (GUI) Matlab. Dengan adanya program GUI Matlab maka proyeksi untuk penambahan gardu induk dapat dilakukan dengan mudah, cepat dan juga tepat serta dapat diaplikasikan untuk wilayah manapun dengan lebih akurat. Berdasarkan hasil simulasi GUI didapatkan total penambahan kapasitas trafo untuk wilayah Tangerang adalah sebesar 1,6 MVA dengan penambahan gardu distribusi sebanyak 7 unit.

Unduhan

Data unduhan belum tersedia.

Biografi Penulis

Adri Senen, Institut Teknologi PLN

Jurusan Teknik Elektro

Oktaria, Institut Teknologi PLN

Jurusan Teknik Elektro

Christine Widyastuti, Institut Teknologi PLN

Jurusan Teknik Elektro

Referensi

Afrasiabi, M., Mohammadi, M., Rastegar, M., Stankovic, L., Afrasiabi, S., & Khazaei, M. (2020). Deep-Based Conditional Probability Density Function Forecasting of Residential Loads. IEEE Transactions on Smart Grid, 11(4), 3646–3657. https://doi.org/10.1109/TSG.2020.2972513

Djamali, M., Tenbohlen, S., Junge, E., & Konermann, M. (2018). Real-Time Evaluation of the Dynamic Loading Capability of Indoor Distribution Transformers. IEEE Transactions on Power Delivery, 33(3), 1134–1142. https://doi.org/10.1109/TPWRD.2017.2728820

Dwiyoko, G., Sukisno, T., & Damarwan, E. S. (2020). Proyeksi Kebutuhan Energi Listrik Kabupaten Purbalingga Tahun 2030 Menggunakan Software Leap. Jurnal Edukasi Elektro, 4(1), 29–40. https://doi.org/10.21831/jee.v4i1.32043

Firdaus, A. A., Penangsang, O., Soeprijanto, A., & Dimas Fajar, U. P. (2018). Distribution network reconfiguration using binary particle swarm optimization to minimize losses and decrease voltage stability index. Bulletin of Electrical Engineering and Informatics, 7(4), 514–521. https://doi.org/10.11591/eei.v7i4.821

Gde Made Yoga Semadhi Artha, I. (2019). Transformer’s Load Forecasting to Find the Transformer Usage Capacity with Adaptive Neuro-Fuzzy Inference System Method. Journal of Electrical and Electronic Engineering, 7(1), 1. https://doi.org/10.11648/j.jeee.20190701.11

Gligor, A., Vlasa, I., Dumitru, C.-D., Moldovan, C. E., & Damian, C. (2020). Power Demand Forecast for Optimization of the Distribution Costs. Procedia Manufacturing, 46, 384–390. https://doi.org/10.1016/j.promfg.2020.03.056

Handayani, O., Senen, A., Widyastuti, C., & Sukma, D. Y. (2021). Micro-Spatial Electricity Planning in Urban Area Based on Energy Demand. 2021 3rd International Conference on High Voltage Engineering and Power Systems, ICHVEPS 2021, 155–160. https://doi.org/10.1109/ICHVEPS53178.2021.9601086

He, S., & Li, P. (2020). A MATLAB based graphical user interface (GUI) for quickly producing widely used hydrogeochemical diagrams. Chemie Der Erde, 80(4). https://doi.org/10.1016/j.chemer.2019.125550

Hertel, M., Ott, S., Neumann, O., Schäfer, B., Mikut, R., & Hagenmeyer, V. (2022). Transformer Neural Networks for Building Load Forecasting. (December), 0–7. Retrieved from https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014

Lekshmi, M., & Subramanya, K. N. A. (2019). Short-Term Load Forecasting of 400kV Grid Substation Using R-Tool and Study of Influence of Ambient Temperature on the Forecasted Load. 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 1–5. https://doi.org/10.1109/ICACCP.2019.8883005

McNeil, M. A., Karali, N., & Letschert, V. (2019). Forecasting Indonesia’s electricity load through 2030 and peak demand reductions from appliance and lighting efficiency. Energy for Sustainable Development, 49, 65–77. https://doi.org/10.1016/j.esd.2019.01.001

Meng, Z. (2022). Bagging Based Multi-Source Learning and Transfer Regression for Electricity Load Forecasting. 49(2).

Nnachi, G. U., Akumu, A. O., Richards, C. G., & Nicolae, D. V. (2018). Estimation of no-Load Losses in Distribution Transformer Design Finite Element Analysis Techniques in Transformer Design. 2018 IEEE PES/IAS PowerAfrica, PowerAfrica 2018, 527(1), 527–531. https://doi.org/10.1109/PowerAfrica.2018.8521142

Otong, M. (2019). Rekonfigurasi Jaringan Distribusi Menggunakan Algoritma Genetika di Interkoneksi Penyulang Pakupatan dan Palima pada Beban Prioritas untuk Mengurangi Rugi Daya dan Jatuh Tegangan. https://doi.org/10.36055/setrum.v8i2.6796

Oulasvirta, A., Dayama, N. R., Shiripour, M., John, M., & Karrenbauer, A. (2020). Combinatorial Optimization of Graphical User Interface Designs. Proceedings of the IEEE, 108(3), 434–464. https://doi.org/10.1109/JPROC.2020.2969687

Prakash, K., Islam, F. R., Mamun, K. A., & Pota, H. R. (2020). Configurations of Aromatic Networks for Power Distribution System. Sustainability, 12(10), 4317. https://doi.org/10.3390/su12104317

Sbravati, A., Oka, M. H., Maso, J. A., & Valmus, J. (2018). Enhancing Transformers Loadability for Optimizing Assets Utilization and Efficiency. 2018 IEEE Electrical Insulation Conference (EIC), (June), 144–149. https://doi.org/10.1109/EIC.2018.8481063

Senen, A., Widyastuti, C., Handayani, O., & Putera, P. (2021). Development of micro-spatial electricity load forecasting methodology using multivariate analysis for dynamic area in tangerang, indonesia. Pertanika Journal of Science and Technology, 29(4), 2565–2578. https://doi.org/10.47836/PJST.29.4.18

Zhang, J., Liu, K., Liu, G., Xu, B., & Kang, Y. (2018). Research on the Influence of Primary Load Imbalance on the Combined Transformer’s Error. 2018 International Conference on Power System Technology (POWERCON), (201804270000511), 1504–1511. https://doi.org/10.1109/POWERCON.2018.8602069

Zhang, S., Wang, Y., Zhang, Y., Wang, D., & Zhang, N. (2020). Load probability density forecasting by transforming and combining quantile forecasts. Applied Energy, 277, 115600. https://doi.org/10.1016/j.apenergy.2020.115600

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Diterbitkan

2023-09-21

Cara Mengutip

Senen, A., Handayani, O., & Widyastuti, C. (2023). Penambahan Gardu Distribusi Berdasarkan Pertumbuhan Beban Listrik Menggunakan GUI Matlab di Wilayah Tangerang. Jurnal Teknik: Media Pengembangan Ilmu Dan Aplikasi Teknik, 22(1), 01–09. https://doi.org/10.55893/jt.vol22no1.499