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Analisis Kondisi Transformator Tenaga dan Rekomendasi Tindak Lanjut Berbasis Health Index I Made Gusmara Nusaman; Musa Partahi Marbun
TIN: Terapan Informatika Nusantara Vol 6 No 10 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i10.9430

Abstract

Transformers are essential equipment in electric power transmission systems that function to adjust voltage levels according to system requirements. Over the course of operation, transformer performance may degrade due to aging, loading conditions, and the frequency of disturbances, which can potentially reduce the reliability of power supply. This study examined 22 units of 150 kV GIS-type power transformers with a capacity of 60 MVA located across 8 GIS substations, using a Health Index–based analysis method. The application of the Health Index showed that 19 transformers (86.36%) were in the Good–Very Good category, while 3 units (13.64%) were in the Caution–Very Poor category. Validation was carried out on samples representing the Very Good, Poor, and Very Poor categories, and it demonstrated strong agreement between the Health Index values and the results of inside check, laboratory tests, as well as operational history and further in-depth testing. Correlation analysis indicated a strong negative relationship between the Health Index and transformer operating age (r = –0.821), suggesting that increasing operation age significantly contributes to a decrease in the Health Index value. Based on the analysis, three follow-up actions are recommended: normal operation, operation with intensive and periodic monitoring, and transformer replacement, representing the overall health levels of the transformers in the study sample.
Analisa Prediksi Produksi Energi PLTA Menggunakan Metode Simulasi Monte Carlo Sinda Ramadhana Akbar; Musa Partahi Marbun
TIN: Terapan Informatika Nusantara Vol 7 No 4 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i4.10913

Abstract

Hydropower Plants (HPPs) are one of the most important renewable energy sources supporting the national electricity supply. However, electricity generation in HPPs is influenced by various factors, including hydrological conditions, water discharge, rainfall, equipment reliability, maintenance schedules, and power system demand, resulting in inherent uncertainty in energy production. This study aims to predict the electricity generation of the Kerinci Merangin Hydropower Plant over a 30-year period using the Monte Carlo Simulation method and to analyze its Capacity Factor (CF). A quantitative approach was employed using historical electricity generation data from the Kerinci Merangin Hydropower Plant. The research procedure included probability distribution analysis, cumulative distribution development, random number generation using Microsoft Excel, and 360 simulation iterations representing 30 years of plant operation. The simulation results indicate a total projected electricity generation of 42,950,284,934 kWh, with an average annual generation of 1,431,676,164 kWh and an average monthly generation of 119,306,347 kWh. The average Capacity Factor was 46.68%, with monthly values ranging from 14.91% to 92.22%, indicating a moderate to high level of plant capacity utilization. Evaluation under the annual Take-or-Pay scheme shows that most simulation results meet the contractual energy target, although seven years are predicted to fall below the target due to variations in hydrological conditions, water discharge, and plant operational reliability. The findings demonstrate that the Monte Carlo Simulation method is effective for long-term electricity generation forecasting and can serve as a valuable tool for operational planning, maintenance scheduling, water resource management, and business strategy development to enhance system reliability and support informed decision-making.