Penentuan Kriteria Kapasitas Transformator Berdasarkan Proyeksi Kebutuhan Energi secara Mikrospasial

ADRI SENEN, HASNA SATYA DINI, DWI ANGGAINI, PERDANA PUTERA

Sari


ABSTRAK

Proyeksi energi memiliki peran penting dalam perencanaan pengembangan sistem distribusi listrik. Penelitian ini bertujuan untuk menentukan lokasi, jumlah dan penambahan kapasitas transformator yang diperlukan di area jaringan Tangerang. Metode prakiraan proyeksi energi dilakukan secara mikrospasial dengan membagi area layananan dalam bentuk grid – grid yang kecil (kelurahan). Selanjutnya pengelompokan (clustering) dilakukan berdasar karakteristik geografis, demografi, ekonomi dan kelistrikan wilayah untuk memperkirakan kerapatan beban. Hasil clustering yang terdiri dari 100 kelurahan, terkelompok menjadi 5 cluster dengan pertumbuhan beban per cluster rata-rata sebesar 8,4 %. Hasil perhitungan kapasitas transformator untuk wilayah Tangerang untuk 10 tahun adalah 250 kVA, 630 kVA, 1000 kVA dan 1250 kVA, dengan asumsi pembebanan transformator maksimal 80 %. Disamping itu prakiraan beban pada tingkatan transformator distribusi mengalami penambahan 3.064 unit gardu distribusi.

Kata kunci: Prakiraan Beban, Transformator , Micro-spatial, Cluster, Tangerang

 

ABSTRACT

Energy projection has important role in the planning of electricity distribution systems development. This study set out to investigate location, number and capacity of the transformers in Tangerang network area. A microspatial energy projection forecasting method was used by dividing the service area into small grids. Furthermore, clustering is carried out based on geographical, demographic, economic and electrical characteristics of the region to predict the load density. The clustering results consist of 100 grids grouped into 5 clusters with an average load growth 8.4% per cluster. As the result, the transformers capacity for the Tangerang area for the next 10 years are 250 kVA, 630 kVA, 1000 kVA and 1250 kVA, with the assumption that the maximum transformer loading is 80%. In addition, the estimated load at the distribution transformer level has an additional 3,064 distribution substations.

Keywords: Load Forecasting, Transformer, Micro-spatial, Cluster, Tangerang


Kata Kunci


Prakiraan Beban; Transformator; Micro-spatial; Cluster; Tangerang

Teks Lengkap:

PDF

Referensi


Bracale, A., Carpinelli, G., & De Falco, P. (2019). Probabilistic risk-based management of distribution transformers by dynamic transformer rating. International Journal of Electrical Power & Energy Systems, 113, 229–243. https://doi.org/10.1016/j.ijepes.2019.05.048

Bunn, M., Das, B. P., Seet, B.-C., & Baguley, C. (2019). Empirical Design Method for Distribution Transformer Utilization Optimization. IEEE Transactions on Power Delivery, 34(4), 1803–1813. https://doi.org/10.1109/TPWRD.2019.2926328

Farahzad, K., Shahbahrami, A., & Ashouri, M. (2020). Optimal Capacity Determination For Electrical Distribution Transformers Based On IEC 60076-7 And Practical Load Data. International Journal of Engineering and Manufacturing, 10(1), 1–11. https://doi.org/10.5815/ijem.2020.01.01

Fox, J., & Weisberg, S. (2018). An R Companion to Applied Regression. SAGE Publication Inc.

Gajowniczek, K., & Ząbkowski, T. (2018). Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting. Complexity, 2018, 1–21. https://doi.org/10.1155/2018/3683969

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

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariat Statistical Analysis (6th ed.). Pearson.

Kampezidou, S. I., & Grijalva, S. (2016). Distribution transformers short-term load forecasting models. In 2016 IEEE Power and Energy Society General Meeting (PESGM) (pp. 1–5). IEEE. https://doi.org/10.1109/PESGM.2016.7741174

Pasculescu, D., Pana, L., Pasculescu, V. ., & Deliu, F. (2019). Economic criteria for optimizing the number and load factor of mining transformers. Mining of Mineral Deposits, 13(2), 1–16. https://doi.org/10.33271/mining13.02.001

Raza, M. Q., Mithulananthan, N., Li, J., & Lee, K. Y. (2020). Multivariate Ensemble Forecast Framework for Demand Prediction of Anomalous Days. IEEE Transactions on Sustainable Energy, 11(1), 27–36. https://doi.org/10.1109/TSTE.2018.2883393

Rencher, A. C. (2002). Methods of Multivariate Analysis (second edi). John Wiley & Sons, Inc.

Senen, A. (2020). Pengembangan Metodologi Prakiraan Beban Listrik Sektoral Secara Mikrospasial. Journal Kajian Ilmu Dan Teknologi, 9(2), 234–243. https://doi.org/10.33322/kilat.v9i2.1016

Shahzadeh, A., Khosravi, A., & Nahavandi, S. (2015). Improving load forecast accuracy by clustering consumers using smart meter data. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE. https://doi.org/10.1109/IJCNN.2015.7280393

Sun, X., Ouyang, Z., & Yue, D. (2017). Short-term load forecasting based on multivariate linear regression. In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) (pp. 1–5). IEEE. https://doi.org/10.1109/EI2.2017.8245401

Vuluvala, M. R., & Saini, L. M. (2018). Load balancing of electrical power distribution system: An overview. In 2018 International Conference on Power, Instrumentation, Control and Computing (PICC) (pp. 1–5). IEEE. https://doi.org/10.1109/PICC.2018.8384780

Widyastuti, C., Senen, A., & Handayani, O. (2020). Micro-Spatial Electricity Load Forecasting Using Clustering Technique. In 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE) (pp. 17–21). IEEE. https://doi.org/10.1109/ICIEE49813.2020.9277274

Xu, X., Xue, F., Wang, X., Lu, S., Jiang, L., & Gao, C. (2020). Upgrading Conventional Distribution Networks by Actively Planning Distributed Generation Based on Virtual Microgrids. IEEE Systems Journal, 1–12. https://doi.org/10.1109/JSYST.2020.2999560

Ye, C., Ding, Y., Wang, P., & Lin, Z. (2019). A Data-Driven Bottom-Up Approach for Spatial and Temporal Electric Load Forecasting. IEEE Transactions on Power Systems, 34(3), 1966–1979. https://doi.org/10.1109/TPWRS.2018.2889995




DOI: https://doi.org/10.26760/elkomika.v10i1.200

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

Publisher:

Department of Electrical Engineering Institut Teknologi Nasional Bandung

Address: 20th Building  Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124

Contact: +627272215 (ext. 206)

Email: jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________


Free counters!

Web

Analytics Made Easy - StatCounter

Statistic Journal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License