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Jurnal Teknik Elektro (JTE)
Published by Universitas Negeri Surabaya
ISSN : 22525017     EISSN : 29870089     DOI : https://doi.org/10.26740/jte.v14n1
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 467 Documents
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Analisis Sistem Kontrol Bidirectional Converter dengan Algoritma Electric Charged Particles Optimization (ECPO) terhadap Perubahan Tegangan Input Baterai Aditya Rendra Pratama; Rifqi Firmansyah
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p228-237

Abstract

Battery charging and discharge systems require an ideal control system for improved performance and extended battery life as it is crucial in the usage of renewable energy and electric cars. One of the main problems of this system is how to achieve the best control of the bidirectional converter for power flow regulation in the source and battery in order to make it of fast response nature, high efficiency, and good stability during various operating conditions. In this paper, it is proposed to make use of the utilization of the application of the Electric Charged Particles Optimization (ECPO) algorithm as the optimizing method in the control system of the bidirectional converter. ECPO is a population-based method based on the inspiration of the interaction between electrically charged particles that can find optimal solutions quicker and more economically than traditional optimization methods. The simulation test under various conditions with Simulink shows that the settling time is smaller, response and steady state error almost 0 in the ECPO system. According to the test results, the ECPO system can confirm a measurable enhancement in the stability and robustness of the system.
Optimalisasi Set Point RPM Fan ID pada Industri Semen Menggunakan Algoritma XGBoost Regressor Berbasis Parameter Operasional dan Komposisi Kimia James Tulende; Lusia rakhmawati; Bambang Suprianto
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p185-194

Abstract

Industri semen merupakan salah satu sektor industri berenergi tinggi yang membutuhkan pengelolaan operasi efisien untuk menjaga efisiensi energi dan stabilitas proses pembakaran pada kiln. Salah satu peralatan vital dalam sistem kiln adalah Induced Draft (ID) Fan yang berfungsi mengatur tekanan negatif dan aliran gas buang selama proses pembakaran. Penentuan setpoint kecepatan putar (RPM) ID Fan yang masih dilakukan secara manual berdasarkan pengalaman operator sering kali sulit menyesuaikan fluktuasi kondisi operasional dan karakteristik bahan baku. Penelitian ini bertujuan untuk mengoptimalkan penentuan setpoint RPM ID Fan menggunakan algoritma XGBoost Regressor berbasis 20 parameter operasional kiln. Dataset historis sebanyak 2.143 observasi diproses melalui alur prapemrosesan data, seleksi variabel, serta pembagian data training dan testing dengan rasio 80:20. Kinerja model dievaluasi menggunakan Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Koefisien Determinasi (). Hasil penelitian menunjukkan bahwa model XGBoost Regressor dengan konfigurasi estimators 100 dan learning rate 0,1 menghasilkan performa terbaik dengan nilai sebesar 96,86%, MAE sebesar 0,7068 RPM, dan RMSE sebesar 0,9546 RPM, mengungguli Regresi Linear Dasar ( = 96,34%) serta menawarkan stabilitas regularisasi yang lebih baik dibanding Random Forest Regressor ( = 97,39%). Hasil prediksi diintegrasikan ke dalam dashboard interaktif Google Looker Studio untuk memberikan visualisasi real-time yang mendukung pengambilan keputusan berbasis data (data-driven decision-making) bagi Control Room Operator (CRO) guna meningkatkan efisiensi energi listrik dan stabilitas operasi kiln. Kata kunci: industri semen, Induced Draft Fan, RPM, XGBoost Regressor, machine learning, Google Looker Studio.
Sistem Peringatan Dini Gangguan Gardu Induk Berbasis IoT pada Post Keamanan GIS 150kV Gunungsari Ghina Fatma Cahyanni; I Made Niantara; Farid Baskoro
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p195-205

Abstract

This study aims to address the limited auditory range of conventional alarms at the Gunungsari 150 kV GIS by developing an Internet of Things (IoT)-based early warning system. Using a descriptive-analytical experimental study approach, this research conducted systematic observations and functional testing of a prototype that integrates an ESP32 microcontroller and a KY-037 sound sensor. Key findings indicate that setting the threshold at 60 effectively distinguishes alarm signals from background noise, with notification transmission to the Telegram app taking less than 5 seconds. The system architecture, which separates the logic for local visual indicators (LEDs) from network notifications, ensures the reliability of alerts even in the event of internet connectivity issues. These findings contribute to strengthening the “human-in-the-loop” monitoring model, which enhances the effectiveness of remote monitoring by substation personnel. The practical implication is the creation of a more responsive and accountable energy infrastructure protection system. Further research is recommended to integrate network redundancy modules and autonomous power sources to anticipate the risk of total power outages.
Pengembangan Sistem Pompa Sump Otomatis Berbasis IoT untuk Saluran Cable Duct James Tulende; Lukman Hakim Febriansyah; Dwi Bagus Aminudin; Lusia Rakhmawati
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p219-227

Abstract

Sump pumps play a critical role in preventing water accumulation in cable ducts at industrial facilities by automatically removing water entering the cable duct. Conventional sump pump systems generally rely on manual monitoring and have limited protection features, which may reduce operational reliability and increase the risk of pump failure. This study proposes an ESP32-based automatic sump pump monitoring and control system integrated with the Arduino IoT Cloud for real-time monitoring and remote operation. The developed system employs a float switch to detect the water level, a flow switch to verify water flow, and a Thermal Overload Relay (TOR) to protect the pump motor from overload conditions. The implemented control algorithm includes automatic pump operation, Flow Fail detection with a timeout of 10 s, Over Time protection limiting continuous operation to 300 s (5 min), automatic fault reset after 8 h for non-overload faults, and a 5 s recovery delay following overload clearance. Experimental results demonstrate that the proposed system successfully performs automatic pump control, detects abnormal operating conditions, and provides reliable fault protection. Furthermore, the integration with the Arduino IoT Cloud enables real-time monitoring of pump status, water level, flow condition, and fault information, as well as remote manual control through a wireless network. The proposed system offers a practical, low-cost, and reliable solution for improving safety, monitoring capabilities, and operational efficiency of sump pump systems for water management in industrial cable ducts.
Optimasi Konsumsi Energi Kendaraan Listrik Menggunakan Kontrol MIMO Berbasis Deep Reinforcement Learning pada Kondisi Berkendara Dinamis Yusuf Rahman Maulana; Puput Wanarti Rusimamto
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p206-218

Abstract

Konsumsi energi pada kendaraan listrik sangat dipengaruhi oleh berbagai faktor dinamis sepertigaya mengemudi, kondisi lalu lintas, topografi jalan, yang dapat mengurangi jangkauan kendaraanhingga 15-25%. Manajemen energi yang tidak efisien dapat secara drastis mengurangi jarak tempuhefektif kendaraan, menurunkan kepraktisan dan keandalannya dalam penggunaan sehari-hari. Strategimanajemen energi berbasis pembelajaran mendalam telah menunjukkan peningkatan efisiensi hingga20% dibandingkan dengan rute perjalanan konvensional dan 12% dibandingkan dengan eco-drivingstandar.Penelitian ini berfokus pada optimasi energi pada sistem powertrain kendaraan listrik, yangmeliputi motor listrik, baterai, dan kontroler. Pengembangan, pelatihan, dan pengujian sistem kontroldilakukan sepenuhnya dalam lingkungan simulasi perangkat lunak MATLAB/Simulink. Metodemachine learning yang digunakan terbatas pada algoritma Deep Reinforcement Learning. KontrolMIMO yang digunakan terbatas hanya tiga input dan juga tiga output yang mementingkan tiga faktorutama dalam kendaraan listrik. Model kendaraan listrik yang digunakan dalam simulasi merupakanmodel matematis yang disederhanakan dan tidak mempertimbangkan semua dinamika kendaraan secaramendetail seperti degradasi baterai atau pengaruh suhu ekstrem. Penelitian ini berfokus pada optimasienergi pada sistem powertrain kendaraan listrik, yang meliputi motor listrik, baterai, dan kontroler. 2)Pengembangan, pelatihan, dan pengujian sistem kontrol dilakukan sepenuhnya dalam lingkungansimulasi perangkat lunak MATLAB/Simulink. 3) Metode machine learning yang digunakan terbataspada algoritma Deep Reinforcement Learning (DRL). 4) Kontrol MIMO yang digunakan terbatas hanyatiga input dan juga tiga output yang mementingkan tiga faktor utama dalam kendaraan listrik. 5) Modelkendaraan listrik yang digunakan dalam simulasi merupakan model matematis yang disederhanakandan tidak mempertimbangkan semua dinamika kendaraan secara mendetail seperti degradasi bateraiatau pengaruh suhu ekstrem.
Optimasi Pengendalian PID pada Load Frequency Control Menggunakan Artificial Hummingbird Algorithm Gilang Esa Muhammad; Rifqi Firmansyah
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p238-248

Abstract

Load Frequency Control (LFC) is an important part of the electrical power system that functions to maintain frequency stability when load changes occur. Frequency instability can reduce system reliability and disrupt the performance of connected equipment. One common method to improve system stability is to use a Proportional-Integral-Derivative (PID) controller. However, conventional PID parameter tuning often fails to produce optimal performance. This study aims to optimize PID control parameters using the Artificial Hummingbird Algorithm (AHA), a metaheuristic algorithm inspired by the flight and foraging behavior of hummingbirds. The implementation was carried out using MATLAB with the Integral of Time-weighted Absolute Error (ITAE) objective function to minimize system errors. The AHA algorithm was modified to display the evolution of fitness values and convergence to the optimal solution during the iteration process. The simulation results show that the PID controller optimized with AHA is capable of producing a faster system response, has lower Overshoot, and achieves a more stable condition compared to conventional methods. These findings prove that the Artificial Hummingbird Algorithm is an effective and efficient optimization method for tuning PID control parameters in Load Frequency Control systems.
PERANCANGAN SISTEM MONITORING DAN PROTEKSI GANGGUAN INTERNAL BERBASIS IoT PADA CAPACITOR BANK Windra Choirul Pratama; Endryansyah Endryansyah; Rifqi Firmansyah
JURNAL TEKNIK ELEKTRO Vol. 15 No. 3 (2026): Vol 15 No 3 (2026): SEPTEMBER 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v15n3.p249-256

Abstract

Capacitor banks are used in industrial electrical systems to improve power factor and voltage stability. However, internal disturbances such as overvoltage, undervoltage, overcurrent, and overheating can reduce reliability and increase the risk of equipment damage. This study develops an Internet of Things (IoT)-based monitoring and protection system for capacitor banks using an ESP32 microcontroller. The prototype integrates a ZMPT101B AC voltage sensor, ACS712 AC current sensor, DHT22 temperature and humidity sensor, relay, buzzer, LCD, and Blynk dashboard. The ESP32 reads the sensors in real time, compares the measurements with predefined thresholds, and activates alarm and relay protection when abnormal conditions are detected. Testing showed that the system could monitor voltage, current, and temperature continuously, visualize data locally and remotely, and support automatic protection against internal electrical faults. During normal observation, voltage ranged from 220.05 V to 224.62 V, current from 10.56 A to 63.73 A, and temperature from 28.96 °C to 35.63 °C. The proposed system provides practical remote supervision and early fault response for capacitor bank panels.

Page 47 of 47 | Total Record : 467

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