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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE) Jurnal Pendidikan Teknologi dan Kejuruan JTEV (Jurnal Teknik Elektro dan Vokasional JURNAL NASIONAL TEKNIK ELEKTRO PROtek : Jurnal Ilmiah Teknik Elektro Syntax Literate: Jurnal Ilmiah Indonesia CYCLOTRON Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Electrices : Jurnal Otomasi Kelistrikan dan Energi Terbarukan Jurnal Impresi Indonesia CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi International Journal Science and Technology (IJST) Jurnal Ilmiah Teknik Elektro International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE) Jurnal Surya Energy
Budi Sudiarto
Departemen Teknik Elektro, Fakultas Teknik, Universitas Indonesia, Depok, Jawa Barat, Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences Other

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A Design of Economically Feasible Hybrid Energy System with Renewable Energy Ratio Priority Sibarani, Michael Bonardo Siswono; Jufri, Fauzan Hanif; Samual, Muhammad Gillfran; Widayat, Aditya Anindito; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 2 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i2.60

Abstract

The reduction of fossil fuels which produce CO2 emission that damage the environment, can be done by implementing renewable energy-based power generations, such as solar and wind. This research designs a hybrid energy system by optimizing the use of existing diesel generators through the integration of renewable energy sources, such as solar photovoltaic and micro wind turbine, and is equipped with an energy storage system. This research uses HOMER Pro software to determine the optimal capacity of hybrid system components, and to calculate the cost of energy (CoE). Furthermore, the hybrid system configuration is analyzed by applying several objectives. The objectives of the hybrid system design are to prioritize a maximum renewable energy penetration ratio within permitted annual capacity shortage and with the CoE lower than the existing CoE. The research results show that the proposed hybrid energy system can provide a renewable energy penetration ratio of 57.1% with CoE of IDR 3,510/kWh.
Optimal Battery Energy Storage System Placement Strategy in Central Java Electrical System for Voltage and Losses Improvement Fikry, Hafizh Al; Samual, Muhammad Gillfran; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 3 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i3.75

Abstract

Over the past few decades, advances in energy storage technology, particularly in the form of Battery Energy Storage Systems (BESS), have provided innovative solutions to address various challenges in the power grid such as voltage fluctuations and high levels of losses, which negatively impact the efficiency and quality of electricity provision. BESS has advantages over other energy storage technologies such as having lower costs, faster response times to power equipment or devices, and increased efficiency and flexibility. The purpose of this research is to determine the optimal capacity and location of the placement of BESS to get an improvement in the voltage profile and losses in the Central Java Province power system. In this study, BESS is incorporated into the Jelok substation based on the calculation method under day and night conditions, which will be sought for the most optimal placement. After getting the most optimal placement, the optimal BESS capacity based on the calculation method, 15 MWh, and 25 MWh will be compared. The effect of optimum BESS placement and sizing of up to 0.0035 pu, and reduce losses up to 1.87 MW.
Analysis of Transformer Oil Condition of Dissolved Gas Analysis (DGA) Testing Results with Modified Conventional Methods Budhihadi, Randy Purnawan; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.72

Abstract

Power transformers are one of the most widely used important components in the electric power system. Dissolved Gas Analysis (DGA) testing is used to diagnose transformer failures before more severe damage occurs by analyzing gas indicators dissolved in transformer oil through several methods, one of which uses conventional methods. However, based on most of the tests conducted by researchers, the detection accuracy of the conventional method is still quite low. Therefore, this research has the main objective of identifying the weaknesses of one of the conventional methods, namely the Rogers Ratio method. This research method uses modifications to the fault diagnosis flow chart which is then applied in the interpretation of DGA test results on power transformers in the case of the GSUT #1 20/11 kV Transformer of Manokwari Gas Engine Power Plant (GEPP). Based on the results of this research, the previous method cannot diagnose the fault (Undetermined) while after being modified it can diagnose the “Thermal Fault 150-200OC. When compared with other conventional methods that have been tested such as the interpretation of the Duval Triangle method, the results of diagnosing “Thermal Fault < 300OC”, it means that in general can be known that there has been a thermal disturbance in the internal transformer at temperatures below 300OC. Thus the results of the modified interpretation of the Rogers Ratio method are better than before so that it can be applied as an additional technique for interpreting DGA test data
Techno-Economic Optimization Study of Renewable Energy Planning in Buru Island Electricity System Z Day, Faizatul Hasanah; Samual, Muhammad Gillfran; Garniwa, Iwa; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.73

Abstract

One of the strategies to achieve Indonesia's NDC target in 2030 is through the development of renewable energy power plants, and the transition from fossil fuels to renewable energy. The use of diesel power plants, especially with the case on Buru Island as the only electricity supply, contributes to the production of emissions, and increases the Cost of Energy (CoE) of the utility system. On the other hand, Buru Island is rich in renewable energy potential, such as geothermal, hydropower, bioenergy, and solar energy. This study aims to design an optimal power generation system on Buru Island by considering the renewable energy mix, financial feasibility, reduction in the CoE of local electricity system, reduction in CO2 emissions, and the potential load growth of the local industry, i.e. fisheries industry sector. This study utilizes HOMER software to obtain a power generation scenario that can supply the load with the most optimal renewable energy penetration, the lowest Levelized CoE (LCOE), and the lowest CO2 emissions. Seven electrical systems on Buru Island were implemented to form 4 systems, namely an integrated system of 4 previously distributed systems, and 3 other distributed systems. The result of this research gives out the most optimum configuration of hybrid or complete renewable energy-based power plant configuration for each system. The configurations can reduce the CoE up to 20.17 cUSD/kWh, and up to zero CO2 emission.
Optimization of Heat Rate and Greenhouse Gas Emission Reduction at Coal-Fired Power Plants in Indonesia Through Machine Learning Modeling Setyawan, Ariandiky Eko; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.77

Abstract

This study aims to develop predictive models for the heat rate of coal-fired steam power plants (CFSPPs) in Indonesia using various machine learning techniques and to identify factors influencing greenhouse gas emissions, specifically CO2. Techniques used include Linear Regression, Lasso Regression, Polynomial Regression, Ridge Regression, Support Vector Regression, Random Forest Regression, Gradient Boosting Regression, Elastic Net Regression, AdaBoost Regression, Neural Network Regression, Decision Tree Regression, and Extra Trees Regression. The data consists of 468 performance test results from CFSPPs, covering operational parameters such as boiler type, ambient temperature, flue gas temperature, and unburned carbon. Analysis shows that the Extra Trees Regression model provides the best performance with an R-squared value of 0.947, MAE of 133.648, MSE of 34694.478, and RMSE of 186.265 for heat rate modeling, and an R-squared value of 0.993, MAE of 21.02, MSE of 1402.858, and RMSE of 37.455 for CO2 emissions modeling, demonstrating high accuracy and good generalization. Significant factors influencing the heat rate include Gross Power Output (GPO), Net Power Output (NPO), load percentage, boiler type, coal HHV, coal consumption, and operational duration. This model is implemented using the Postman application for real-time heat rate and CO2 emissions prediction, facilitating integration with CFSPP’s operational systems. The research results indicate that the application of machine learning can improve energy efficiency and reduce CO2 emissions, supporting Indonesia's Nationally Determined Contribution (NDC) targets. This study provides new insights into the application of machine learning in the power generation industry and offers recommendations for further implementation and research.
Feature Importance in Predicting Generator Rotor Thermal Sensitivity: A Random Forest-LSTM Approach Wardhana, Aryatama Wisnu; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.90

Abstract

Thermal sensitivity incidents on generator rotors at Muara Tawar Power Plant have increased over the past five years, which will have an impact on the overall performance of the power plant. The general method of conducting thermal sensitivity testing requires the generating unit to be in a certain operating pattern, thus limiting the analysis of anticipating events in real time. Correlation analysis between excitation current variables, reactive power, vibration, and temperature needs to be carried out periodically. The acquisition of these operating parameters was carried out on three generator rotors for 14 days per minute and will be implemented into a machine learning model. This study uses the Random Forest model to predict vibrations on the rotor and determine featureimportance values, with the addition of Long Short-Term Memory (LSTM) modeling to predict future trends based on feature importance. The results show that the Random Forest model can predict vibrations in the rotor and determining the importance of the features used, with an average evaluation metric RMSE of 0.92% and R2 of 81.62% on the exciter side, and RMSE of 2.75% and R2 of 61.42% on the turbine side. The LSTM model also demonstrates good capability in predicting future trends in thermal sensitivity identification based on exciter current features with an RMSE of 7.29% and for reactive power features of 6.52%, indicating that the proposed modeling implementation allows a better understanding of the variables relevant to thermal sensitivity, thus predicting them in the future can produce comprehensive operation and maintenance strategies.
Grid Import Optimization with Adaptive Deep Reinforcement Learning for PV-Battery Systems Karim, Romi Naufal; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.98

Abstract

This article explores the application of Deep Reinforcement Learning (Deep RL) to optimize energy management in photovoltaic (PV) and battery systems. The new framework presented here includes important innovations such as Rule-Based Action Smoothing for system performance consistency, PPO Multi-House Training to generalize across a wide range of energy usage patterns, and Post-Controller Integration to deal with real-time operational issues. While the dataset originates from Ireland, the model is adapted to align with Indonesia's dual-tariff system and local energy regulations. Simulation results demonstrate substantial cost savings, with reductions of up to 85.28% in stable scenarios and 18.26% in high-variability environments. These results highlight the flexibility and resilience of the methodology for using renewable energy to reduce costs and increase system efficiency. The model is, therefore, scalable for the implementation of intelligent energy systems in the residential context to support Indonesia's renewable energy goals and demonstrate its applicability to a broad range of scenarios.
Day-Ahead Solar Power Forecasting Using a Hybrid Model Combining Regression and Physical Model Chain Pongmasakke, Erwin Pauang; Liu, Jian-Hong; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.108

Abstract

Solar power forecasting is essential for integrating PV plants into power grids, ensuring stability and aiding system operators (SOs) in decision-making. However, existing day-ahead models struggle with rapid weather changes, while deep learning models require extensive historical data, making them impractical for new PV plants.This study proposes a hybrid approach combining the XGBoost algorithm for hourly solar irradiance prediction using Numerical Weather Prediction (NWP) data and a physical model to convert irradiance into power. The XGBoost model is periodically retrained via a sliding window mechanism to adapt to dynamic weather conditions.A case study using two years of 271 kWp PV data from NIST (US) and historical NWP data from ECMWF ENS for GHI forecasting, alongside ECMWF HRES for power conversion, demonstrated the method’s effectiveness. Using just one week of historical data for initial training, the model achieved an nRMSE of 13.35%–13.53%, nMAE of 6.9%–7.03%, and nMBE of -2.03% to -0.29%. The proposed approach improves PV forecasting reliability for new plants with limited data, serving as an intermediary solution until sufficient historical data is available for deep learning models.
Optimizing Power Transformer Failure Identification: A Multi-Method Framework Based on Normalized Energy Intensity According to IEEE C57.104-2019 Standards Adapted to Indonesian Power Transformer Characteristics Kurniawan, Wahyu Citra; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.121

Abstract

This research develops and validates a multi-method diagnostic framework by integrating Normalized Energy Intensity (NEI) parameters according to IEEE C57.104-2019 standards adapted for Indonesian power transformer populations. Analysis of 1525 DGA samples from PLN Indonesia transformers reveals significant differences in percentile thresholds compared to North American standards. Using unadapted North American thresholds categorized 68.4% of transformers as critical (DGA Status 3), while adapted thresholds reduced this to 25.1%. Duval Triangle 1 identified Discharge of Low Energy (D1) as the dominant failure type (35.4%), while Duval Pentagon 1 showed dominance of Discharge of High Energy (D2) (39.4%), and Duval Pentagon 2 identified Stray gassing (S) (27.6%) and Overheating without paper carbonization (O) (22.3%). Pearson correlation analysis on transformers with O₂/N₂ ratio ≤ 0.2 showed strong correlations between NEI Oil with ethylene (R = 0.877) and methane (R = 0.845), while NEI Paper strongly correlated with carbon monoxide (R = 0.934). NEI Oil combined with hydrocarbon gas concentrations provided more consistent patterns with multi-method fault identification than NEI Paper. Multi-method validation proved absolute gas concentration methods more reliable than gas ratio methods. This framework improves maintenance efficiency by reducing false alarms and optimizing preventive strategies.
Power Quality Improvement for Voltage Sag Issue in Industrial Customers Putra, Muhamad Mandala; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.131

Abstract

Customer demand today is no longer limited to ensuring the reliability of electricity supply, but also includes the continuity and delivery of high-quality electrical power. One of the main challenges affecting power quality is voltage sag, a condition frequently experienced by industrial customers, particularly at PT. Samator Gas Industri Palembang. This study aims to analyze the causes of voltage sag problems and evaluate the effectiveness of technical solutions. The methodology involves analyzing field observation data, recordings from a Fluke Power Quality Meter (PQM), and simulations of Line-to-Line (LL) and Three-Phase (L-3P) short-circuit faults using ETAP software. The simulation results are evaluated using the ITIC Curve to determine whether the observed voltage levels fall within acceptable operational boundaries or enter the prohibited zone. Simulations were conducted under normal operating conditions by integrating three technical solutions: Static VAR Compensator (SVC), IS-Limiter, and Diesel Rotary UPS (DRUPS). The findings indicate that although SVC can accelerate voltage recovery after a disturbance, its effectiveness is lower compared to the others. The IS-Limiter provides a rapid response to limit fault current and prevent the propagation of disturbances throughout the system. Meanwhile, DRUPS offers the fastest and most reliable voltage recovery, restoring voltage to 100% in less than 20 milliseconds.
Co-Authors Agus R Utomo Agus R. Utomo Agustinus, Alpha Ali, Haidar Alim, Adhipa Tri Setyawan Alvin, Bob Arsyad, Masrajuddin Basyaib, Usamah Budhihadi, Randy Purnawan Dewi ANGGRAENI Dicky Syachreza Himawan Faishal, Fariz Dzaky Fikry, Hafizh Al Hero Gunawan Husnayain, Faiz I Made Ardita I Made Ardita Ihsan Irfanto Imran Syahrizal Iwa Garniwa Jufri, Fauzan Hanif Justinus Dipo Nugroho Karim, Romi Naufal Kurniawan, Wahyu Citra Liu, Jian-Hong M. Muaz Afra Yunardi Marbun, M Masrajuddin Muhammad Arkhan Pradanugraha Mukti, Doni Abdul Mutiah, Fiqi Nasser, Eriko Nasemudin Nasution, Eldi Firmansyah Nurwahono, Lugito Pongmasakke, Erwin Pauang Pradanugraha, Muhammad Arkhan Prasetya, Candra Agung Pratama, Rheo Loridho Purnomo Sidi Priambodo Putra, Muhamad Mandala Rheo Loridho Pratama Rudy Setiabudy Salile, Ahmad Yusuf Samual, Muhammad Gillfran Savira, Resti Setyawan, Ariandiky Eko Sibarani, Michael Bonardo Siswono Tamim, Faizal Wahyudi Hasbi Wardhana, Aryatama Wisnu Widayat, Aditya Anindito Yogi Sepatra Paulus Yunardi, M. Muaz Afra Yus Natali Z Day, Faizatul Hasanah