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This Author published in this journals
All Journal JTEIN: Jurnal Teknik Elektro Indonesia Interdisciplinary Journal of Advanced Research and Innovation
Farid Wijaya
Institut Teknologi PLN
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media

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Transformer Load Calculator & Simulator for 70kV Seduduk Putih Substation: Statistical Analysis and Prediction Nur Aldillah Julita; Syamsir Abduh; Farid Wijaya
Jurnal Teknik Elektro Indonesia Vol 7 No 1 (2026): JTEIN: Jurnal Teknik Elektro Indonesia
Publisher : Departemen Teknik Elektro Fakultas Teknik Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtein.v7i1.790

Abstract

This study aimed to analyze the load patterns of two power transformers at the 70kV Seduduk Putih Substation using descriptive statistical approaches, load analysis, and Multiple Linear Regression based on hourly data throughout 2024. The analysis focused on two main transformers with a capacity of 30 MVA each, employing descriptive statistics and regression methods to identify distribution, trends, and relationships between variables. The results indicate that the average load of the two transformers at the 70kV Seduduk Putih Substation is as follows: Transformer 1 operates at 60% of its maximum load, while Transformer 2 operates at 45% of its maximum load. This suggests that the average loads of both transformers remain below 80%, which is considered normal/safe. However, the peak load analysis shows that Transformer 1 reaches a maximum load of 92.08%, and Transformer 2 reaches 87.03%, indicating that their peak loads exceed 80%. Load data was collected periodically for specific periods, including hourly, monthly, and peak load measurements. Multiple Linear Regression was used to evaluate the relationship between time and transformer load. The findings reveal that the transformers exhibit distinct load patterns for each month, with a moderate correlation to time as indicated by the R-Square value. The peak load analysis identifies critical times with potential overloading risks. This study is expected to serve as a reference for PLN, the operator of the substation, in making operational decisions.
Analysis of the Requirements for an Internet of Things-Based Predictive Monitoring System Using a Requirements Engineering Approach at Gas-Fired Power Plants (PLTMG): A Case Study of Tual City and Southeast Maluku Regency Rico Robert Rangotwat; Farid Wijaya
Interdisciplinary Journal of Advanced Research and Innovation Vol. 4 No. 2 (2026): Interdisciplinary Journal of Advanced Research and Innovation (Issue in Progres
Publisher : Ravine Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58860/ijari.v4i2.105

Abstract

The operational reliability of Gas Engine Power Plants (PLTMG) depends on monitoring systems capable of providing accurate information regarding equipment conditions. However, operational data remain distributed across multiple sources, limiting the implementation of integrated predictive monitoring systems. This study aimed to analyze the requirements of an Internet of Things (IoT)-based predictive monitoring system using a Requirements Engineering approach. A descriptive qualitative method was employed through observations, interviews, and operational document analysis. The results identified six candidate requirements, which were refined into ten system requirements. Using the MoSCoW prioritization method, the requirements were classified into six Must-have (60%), three Should-have (30%), and one Could-have (10%) categories. The prioritized requirements were documented in a System Requirements Specification (SRS) and validated using a Requirement Traceability Matrix (RTM). The analysis indicated that the identified system requirements could improve data availability, monitoring capabilities, analytical capabilities, maintenance decision-making effectiveness, equipment reliability, and power plant availability. This study produced Requirements Engineering artifacts, including the SRS, RTM, conceptual architecture, and Causal Loop Model (CLM), which provide references for developing IoT-based predictive monitoring systems in PLTMG and serve as a foundation for similar studies.
Co-Authors Nur Aldillah Julita Rico Robert Rangotwat Syamsir Abduh