Analisis Fasilitas SCADA Terhadap Kinerja Dispatcher Dalam Mendukung Kinerja First Time Recovery Pada Wilayah UID Jakarta Raya
DOI:
https://doi.org/10.55826/jtmit.v5i3.2129Keywords:
Dispatcher, FTR, SCADA, Simulasi semi real-timeAbstract
PLN UID Jakarta Raya merupakan salah satu unit yang memiliki pelanggan VVIP dan VIP terbanyak di seluruh unit kerja PT. PLN (Persero). PLN UID Jakarta Raya memerlukan solusi untuk dapat melakukan peningkatan dalam hal percepatan dalam penanganan pemadaman tidak terencana. Untuk dapat mencapai tujuan berupa peningkatan percepatan tersebut, maka dibutuhkan KPI yang dapat menjadi tolak ukur dan menjaga agar tetap sesuai dengan tujuan yang diinginkan. Beberapa KPI pada PLN UID Jakarta Raya, terdapat KPI yang tidak tercapai dalam 5 tahun terakhir yaitu FTR (First Time Recovery). KPI tersebut menargetkan penyalaan sebagian gardu maupun keseluruhan ketika terdapat pemadaman tidak terencana dengan durasi dibawah 5 menit. Setelah dilakukan analisa terhadap fasilitas SCADA dan Dispatcher serta dilakukan sebuah simulasi semi real-time menggunakan sistem SCADA Siemens Spectrum 7, didapatkan bahwa untuk mendapatkan kinerja FTR perlu memasikan fasilitas SCADA dapat beroperasi dengan baik dan Dispatcher mampu untuk menganalisa secara cepat dan tepat. Serta diperlukan untuk menghindari adanya faktor eksternal seperti adanya jaringan tidak normal operasi. Hal tersebut karena pada tahun 2026 dari bulan Januari hingga bulan Juni didapatkan kualitas dari fasilitas SCADA untuk membantu mendapatkan FTR di tahun 2026 hanya 37,55 % dari seluruh total gangguan. Selain itu Dispatcher menyumbang kegagalan FTR sebesar 8,22 % dan sebesar 33,88 % disebabkan karena adanya faktor eksternal seperti jaringan tidak normal operasi.
References
[1] Y. Ren, W. Huang, L. Ren, K. Luo, J. Chen, and P. Zhang, Hydro – Wind – Photovoltaic Multi-Energy Centralized Control Systems and Digital Twin-Based Simulation Training Platforms : A Review. 2026.
[2] L. Gu and F. Wang, “International Journal of Electrical Power and Energy Systems Integration and optimisation analysis of PLC and SCADA-HMI – IPC systems in intelligent power distribution monitoring,” Int. J. Electr. Power Energy Syst., vol. 175, no. July 2025, p. 111597, 2026, doi: 10.1016/j.ijepes.2026.111597.
[3] K. Jia, X. Yang, and Z. Peng, “Design of an integrated network order system for main distribution network considering power dispatch efficiency,” 2024.
[4] C. Study, A. Scada, D. C. S. Integration, S. G. Optimization, C. Cycle, and P. Plants, “Journal of Data Science and Analytics,” no. March, pp. 256–294, 2026, doi: 10.63125/ppbq4489.
[5] K. Mohammad and K. Ahsan, “1 st Global Research and Innovation Conference 2025 , Impact of SCADA-GIS Integration on Real-Time Water Distribution Monitoring : A Quantitative Evaluation of Smart Utility Infrastructure,” no. April, pp. 2239–2279, 2025, doi: 10.63125/sp44qz29.
[6] S. Panagi, T. Vermeulen, G. Joseph, and P. Aristidou, “Towards an open-source real-time operator-training platform : Analysis of computational efficiency ⋆,” Electr. Power Syst. Res., vol. 262, no. July 2026, p. 113626, 2027, doi: 10.1016/j.epsr.2026.113626.
[7] U. M. J, A. Sabo, A. I. Araga, A. M. Adua, and A. Usman, “Reliability Assessment Of Power Distribution Systems To Address Frequent Outages And Automatic Line Switches Using SCADA System,” vol. 13, no. 2, pp. 28–48, 2025, doi: 10.5281/zenodo.15276440.
[8] I. N. S. M. A. Scada-to-edge and D. Study, “Journal of Interdisciplinary Studies Ai-Driven Predictive Maintenance For Motor Drives In Smart Manufacturing A Scada-To-Edge,” no. April, pp. 394–444, 2025, doi: 10.63125/gc5x1886.
[9] M. Kermani, B. Adelmanesh, E. Shirdare, C. Alexandra, D. Luca, and L. Martirano, “Intelligent energy management based on SCADA system in a real Microgrid for smart building applications,” Renew. Energy, vol. 171, pp. 1115–1127, 2021, doi: 10.1016/j.renene.2021.03.008.
[10] P. Neis, M. A. Wehrmeister, M. F. Mendes, and J. R. Pesente, “International Journal of Electrical Power and Energy Systems Applying a model-driven approach to the development of power plant SCADA / EMS software,” Int. J. Electr. Power Energy Syst., vol. 153, no. June, p. 109336, 2023, doi: 10.1016/j.ijepes.2023.109336.
[11] T. Ji and X. Xu, “Robotics and Computer-Integrated Manufacturing Exploring the Integration of cloud manufacturing and cyber-physical systems in the era of industry 4 . 0 – An OPC UA approach,” Robot. Comput. Integr. Manuf., vol. 93, no. August 2024, p. 102927, 2025, doi: 10.1016/j.rcim.2024.102927.
[12] K. L. Tomsovic et al., “Methods for Analysis and Quantification of Power System Resilience,” vol. 38, no. 5, pp. 4774–4787, 2023, doi: 10.1109/TPWRS.2022.3212688.
[13] R. Taksana, S. Member, and N. Janjamraj, “Design of Power Transformer Fault Detection of SCADA Alarm Using Fault Tree Analysis , Smooth Holtz – Winters , and L-BFGS for Smart Utility Control Centers,” IEEE Access, vol. 12, no. August, pp. 116302–116324, 2024, doi: 10.1109/ACCESS.2024.3446804.
[14] A. Augello, P. Gallo, E. R. Sanseverino, G. Sciumè, and M. Tornatore, “A Coexistence Analysis of Blockchain , SCADA Systems , and OpenADR for Energy Services Provision,” vol. 10, no. May, 2022, doi: 10.1109/ACCESS.2022.3205121.
[15] R. Taksana, S. Romphochai, and P. Unahalekhaka, “International Journal of Electrical Power and Energy Systems Control-Center analysis of fault events and load forecasting for 115 / 22 kV transformers using outlier filtering and Gradient-Based Holt – Winters parameter tuning,” Int. J. Electr. Power Energy Syst., vol. 172, no. June, p. 111311, 2025, doi: 10.1016/j.ijepes.2025.111311.
[16] M. M. Islam and M. B. Alam, “Sustainable Development and Policy Predictive Analytics for Optimizing Sewerage Sludge Treatment Efficiency and Resource Recovery in Dhaka City,” no. December, pp. 158–200, 2022, doi: 10.63125/t9zdp458.
[17] B. Ni et al., “Energy and AI Intelli-Dispatch-SQL : An LLM-based agent for reliable Text-to-SQL in power dispatching,” Energy AI, vol. 22, no. January, p. 100591, 2025, doi: 10.1016/j.egyai.2025.100591.
[18] K. Sayed, A. G. Abo-khalil, and A. M. Eltamaly, Wind Power Plants Control Systems Based on SCADA System.
[19] C. Series, “Transforming Power System Operations : A Case Study of SCADA / EMS in Bhutan Transforming Power System Operations : A Case Study of SCADA / EMS in Bhutan”, doi: 10.1088/1742-6596/2943/1/012001.
[20] E. F. Infrastructure, “Journal of Innovation MPLS-TP and SONET Security Hardening for Utility SCADA Networks : Threat Modeling and Mitigation Strategies for Energy Fiber Infrastructure,” no. December, pp. 775–812, 2025, doi: 10.63125/7czfg639.
[21] Y. Elyaakouby, A. El-kordy, and A. Tilioua, “Analysis and diagnostic of reverse osmosis membrane fouling using industrial plant data,” Clean. Water, vol. 6, no. December 2025, p. 100247, 2026, doi: 10.1016/j.clwat.2026.100247.
[22] S. Huang and C. Li, “International Journal of Electrical Power and Energy Systems Coordination of preventive , emergency and restoration dispatch against cascading failures for resilience enhancement,” Int. J. Electr. Power Energy Syst., vol. 160, no. February, p. 110136, 2024, doi: 10.1016/j.ijepes.2024.110136.
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