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

Authors

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

DOI:

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

Keywords:

load growth, distribution substation, transformer, graphic user interface

Abstract

Distribution system development Planning is very important in line with the increasing need for electricity loads, attention must be paid to quality of power delivered to consumers. The addition of a distribution network will certainly result in an increase in the capacity and number of transformers and distribution substations. The addition of distribution substations was based on the selection of distribution transformer ratings based on the growth of their load. The distribution transformer loading is made at a maximum of 80% with distributed model. Distribution transformers addition calculation requires an approach to connect the total distribution transformers and distribution substations, namely the average result of the total distribution transformers divided by the total distribution substations, it requires quite complex calculations. To make planning for adding distribution substations easier, you can use the Matlab Graphical User Interface (GUI). With the Matlab GUI program, projections for adding substations can be done easily, quickly, and precisely, and can be applied to any region more accurately. Based on the results of the GUI simulation, it was found that the total additional transformer capacity for the Tangerang area was 1.6 MVA with the addition of 7 distribution substations.

Author Biographies

  • Adri Senen, Institut Teknologi PLN

    Jurusan Teknik Elektro

  • Oktaria, Institut Teknologi PLN

    Jurusan Teknik Elektro

  • Christine Widyastuti, Institut Teknologi PLN

    Jurusan Teknik Elektro

References

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

Additional Files

Published

2023-09-21

How to Cite

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

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