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All Journal Eduvest - Journal of Universal Studies
Suprijadi Suprijadi
Institut Teknologi Bandung, Indonesia
Aerospace Engineering Computer Science & IT Health Professions Neuroscience Social Sciences

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Classification of Health Index of Distribution Substations using Supervised Learning Analysis with SVM Method Donny Zaviar Rizky; Suprijadi Suprijadi
Eduvest - Journal of Universal Studies Vol. 5 No. 1 (2025): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i1.50323

Abstract

As the only electricity provider in Indonesia, PLN is required to be reliable in distributing electrical energy to customers, this is greatly influenced by several PLN assets in the form of distribution substations. The function of this distribution substation is quite crucial in carrying out PLN's business processes to distribute electrical energy. In this study, efforts were made to improve the reliability of distribution substations by knowing the health index in accordance with EDIR PLN No. 017 concerning Distribution Transformer Maintenance Methods Based on Asset Management Principles as the Basis of the Health Index. By knowing the health level of the transformer at the distribution substation, the substation that has substandard criteria can be prioritized for maintenance. The research carried out was to take a sample in 1 month, namely March 2024, from a total of 239 substations, which were then classified using the Support Vector Machine (SVM) method which was compiled in the Python programming language which had been labeled with criteria on each substation. The criteria used in accordance with PLN EDIR No. 017 PLN are Good, Sufficient, Less and Poor. By using Machine Learning according to the Support Vector Machine (SVM) method with Supervised Learning, after the data samples were labeled, then from 239 sample data, it was divided into 2 data, namely training data and test data. In this study, the experiment was carried out with changes in training data by 60%, 70%, 80% and 90% which were then evaluated for accuracy using libary from Python.
Fuzzy Logic Approach for Prediction of Health Index of Feeder Cable Based on Partial Discharge Parameter Hafizh Saftian; Misbakhul Fajri; Suprijadi Suprijadi
Eduvest - Journal of Universal Studies Vol. 5 No. 1 (2025): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i1.50376

Abstract

PT PLN (Persero) as the main provider of electricity in Indonesia, has a strong commitment to improving the reliability of electricity distribution. This reliability is reflected through indicators such as SAIDI (System Average Interruption Duration Index), SAIFI (System Average Interruption Frequency Index), and ENS (Energy Not Served), which are caused by disturbances in the 20 kV extension. Preventive efforts to reduce interruptions are mitigated before interruptions occur by conducting an assessment of the 20 kV feeder cable. This assessment provides important variables that can be processed to predict the condition of the cable and determine the next repair steps. The application of data analysis methods such as Fuzzy Logic in processing technical variables such as PDIV (Partial Discharge Inception Voltage), PDEV (Partial Discharge Extinction Voltage) and Partial Discharge charge values can provide more accurate predictions than conventional calculations. The ability of Fuzzy Logic to overcome uncertain problems, results in another view in assessing the health condition of the repeater cable (Health Index). The results of the research are expected to provide more optimal results as an effort to reduce the frequency of faults to support the achievement of company performance.
Co-Authors Donny Zaviar Rizky Hafizh Saftian Misbakhul Fajri