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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TEKNIK Jurnal Pendidikan Teknologi dan Kejuruan Teknika Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA ELINVO (Electronics, Informatics, and Vocational Education) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology Journal of Electrical Technology UMY Briliant: Jurnal Riset dan Konseptual JST ( Jurnal Sains Terapan ) JTT (Jurnal Teknologi Terpadu) JURNAL INTEGRASI Jurnal Otomasi Kontrol dan Instrumentasi Energi & Kelistrikan JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Indonesian Journal of electronics, electromedical engineering, and medical informatics Technologic Automotive Experiences Journal of Ocean, Mechanical and Aerospace -science and engineering- (JOMAse) Jurnal Nasional Teknik Elektro dan Teknologi Informasi POLIGRID Makara Journal of Technology Power Elektronik : Jurnal Orang Elektro Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics TECHNOLOGIC Agroindustrial Technology Journal (ATJ)
Era Purwanto
Politenik Elektronika Negeri Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Stator Flux Estimator Using Feed-Forward Neural Network for Evaluating Hysteresis Loss Curve in Three Phase Induction Motor Praharsena, Bayu; Purwanto, Era; Jaya, Arma; Rusli, Muhammad Rizani; Toar, Handri; wk, Ridwan
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.235 KB) | DOI: 10.24003/emitter.v6i1.263

Abstract

The operation of induction motors with high performance contributes significantly to the global energy savings but hysteresis loss is one of the factors causing decreased performance. Stator flux density (B) and magnetic field intensity (H) must be plotted to know hysteresis loss quantity. Unfortunately, since the rotor rotates in time series, the stator flux density is unmeasurable quantities, it’s hard to direct sensored this properties because of limited airgap space and costly to install additional instrument. The purpose of this paper is to evaluate the hysteresis loss quantity in induction motor using a novel method of multilayer perceptron feed forward neural network as stator flux estimator and magnetizing current model as magnetic field intensity properties. This method is effective, because it’s non-destructive method, without an additional instrument, low cost, and suitable for real-time motor drive systems. The FFNN estimator response is satisfying because accurately estimate stator flux density for evaluating hysteresis loss quantity including its magnitude and phase angle. By using the proposed model, the stator flux density and magnetizing current can be plotted become hysteresis loss curve. The performance of flux response, speed response, torque response and error deviation of stator flux estimator has been presented, investigated, compared and verified in Simulink Matlab.
Smart adaptive CC-CV charger with PSO-accelerated load identification and fuzzy duty-cycle regulation Indhana Sudiharto; Era Purwanto; Muhamad Milchan; Alifian Nur Rahmadika
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1045-1057

Abstract

This paper presents an adaptive constant-current/constant-voltage (CC-CV) charger architecture, meticulously designed to address a key challenge in smart chargers. This challenge involves recognizing various battery types and applying the appropriate charging profile expeditiously, without requiring user intervention. The system integrates a particle swarm optimization (PSO) algorithm for ultra-fast load identification with a Mamdani-type fuzzy logic controller for precise duty cycle regulation. The PSO mechanism is capable of determining the optimal initial duty cycle in less than 500 milliseconds. Subsequent to this preliminary initiation, the fuzzy logic controller guarantees the effectiveness of current and voltage regulation during the charging phases. The simulation results obtained from this study validate the system's robustness, as evidenced by the consistent maintenance of voltage ripple below ±0.06 V and current ripple below ±0.04 A. These findings demonstrate the efficacy of the proposed approach in achieving fast, stable, and safe multi-load battery charging. The chemistry-agnostic design of the battery pack is extendable to any battery pack following the CC-CV paradigm, making it highly suitable for practical applications that demand flexibility and high reliability.
Design and Implementation of a Transformer Winding Machine with Buck-Boost Converter-Based DC Motor Drive Muhammad Rizani Rusli; Gigih Prabowo; Taufiqurrahman Taufiqurrahman; Arman Jaya; Syechu Dwitya Nugraha; Era Purwanto
Rekayasa Vol 19, No 1: January - April 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v19i1.27486

Abstract

This research presents the design and implementation of a high-frequency transformer winding machine driven by a DC motor with a Buck-Boost converter to produce transformers accurately and efficiently. The system integrates a DC motor controlled by a Buck-Boost converter, which regulates the voltage to the motor, ensuring stable performance during the winding process. The winding machine design includes a stable mechanical platform, a V-belt mechanism for power transmission, and optocoupler sensors for real-time monitoring of winding turns. The Buck-Boost converter stabilizes voltage fluctuations, allowing smooth motor operation under various input conditions, thereby improving machine efficiency and reliability. Experimental tests on the rectifier, Buck-Boost converter, and DC motor demonstrate high efficiency and stable performance, with minimal deviation between calculated and experimental results. Test results show that this machine can perform precise winding across different duty cycles, with optimal speed control and stable operation. Compared to existing transformer winding machines using induction motors or stepper motors, this system offers better control, faster winding speeds, and greater adaptability to different production conditions. The developed machine significantly contributes to industries such as transformer manufacturing and power electronics, with increased productivity, reduced production costs, and improved transformer quality, especially in high-frequency applications such as renewable energy systems and electric vehicle charging.
Design of a Model Predictive Control for Speed Control of a Motor Drive System in an Electric Oil Palm Cutter Indra Ferdiansyah; Fifi Hesty Sholihah; Gigih Prabowo; Era Purwanto; Hairul Faizi Hairulnizam
Agroindustrial Technology Journal Vol. 9 No. 2 (2025): Agroindustrial Technology Journal [ATJ]
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study presents the design of a speed control system for a Motor Drive Permanent Magnet Synchronous Motor (MDPMSM) to achieve a faster and more stable dynamic response in an electric oil palm cutter, supporting the harvesting process of oil palm fruit. Conventional control methods such as Proportional-Integral (PI) controllers, which are commonly applied, still face challenges in parameter tuning and exhibit high sensitivity to speed variations in cutting operations. To overcome these limitations, this research proposes a Model Predictive Control (MPC)-based speed regulation system integrated into a Field-Oriented Control (FOC) structure for a encoderless MDPMSM. The mathematical model of the motor serves as the foundation for designing the predictive algorithm, which can estimate motor speed behavior in real time. Performance evaluation was conducted through simulations under step-response conditions involving sudden speed changes, as well as ramp-response conditions. The simulation results were compared with those of the PI controller to assess the system’s ability in achieving steady-state time, overshoot, and undershoot. The results demonstrate that the MPC-based controller significantly enhances system performance, achieving up to a 60% reduction in settling time, an 84% decrease in overshoot, and a 58% improvement in recovery capability. Moreover, under ramp-response testing, the MPC-based system exhibited a more linear and responsive speed-tracking performance. Therefore, the proposed MPC control design proves to be effective in improving the accuracy and stability of encoderless MDPMSM speed control systems and serves as a reliable alternative for high-precision motor drive control applications, particularly in electric oil palm cutting systems.
Optimizing PI Controller Performance in Ultra Step-Up Converters Using Particle Swarm Optimization Gilang Rizki Saputra; Era Purwanto; Muhammad Rizani Rusli
Jurnal Teknologi Terpadu Vol 13 No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2654

Abstract

The development of electric vehicles (EVs) drives the need for high-voltage ratio DC-DC step-up converters capable of connecting low-voltage battery power sources to electric drive systems and auxiliary systems requiring high voltages. This study proposes a non-isolated ultra step-up converter incorporating a diode–capacitor–inductor (D–C–L) unit at the input and a voltage multiplier cell (VMC) at the output. This topology achieves high voltage gain at low duty cycles while reducing voltage stress on semiconductor devices, enabling the use of lower-rated components and improving overall efficiency. Two control strategies were evaluated: a conventional Proportional–Integral (PI) controller and a PI controller tuned using Particle Swarm Optimization (PSO). MATLAB/Simulink simulations show that the PSO-PI controller outperforms the conventional PI, reducing overshoot from 21.7% to 8.7%, settling time from 227.78 ms to 117.78 ms, and voltage deviation during load changes from ±35 V to ±15 V. Recovery time under disturbances was also shortened from 0.4 s to as low as 0.15 s. These results confirm that PSO-based tuning enhances voltage regulation, transient performance, and robustness, making it a promising solution for ultra step-up converters in EV applications powered by 48 V sources.
Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Estimasi Kecepatan Motor Induksi Dicky Rivaldo Ramdani; Novie Ayub Windarko; Era Purwanto
BRILIANT: Jurnal Riset dan Konseptual Vol 10 No 4 (2025): Volume 10 Nomor 4, November 2025
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v10i4.1990

Abstract

Three-phase induction motors are a major component in industrial machinery due to their simple construction, robustness, relatively low cost, easy maintenance, and high reliability. However, these motors have disadvantages in speed regulation due to their non-linear characteristics, which cause difficulties in maintaining a constant speed when the load changes. This research aims to maintain the speed of induction motors to remain constant by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method.  This ANFIS method goes through the stages of data collection, data processing, ANFIS system design, ANFIS training, validation testing, and finally analyzing the results. The input of the induction motor speed estimation system is through a mathematical equation model in d-q coordinates. The output of this system is speed. In this study, training and testing variations. The smallest RMSE result obtained is with the Trapzeium membership function architecture with the number of epochs 100 of 0.0187542.
Commutation Performance Enhancement of Sensorless BLDC Motor Using Finite Impulse Response Filtering in Back-EMF Detection Ony Asrarul Qudsi; Era Purwanto; Sulis Wanto; Muhammad Rizani Rusli
Jurnal Teknologi Terpadu Vol 14 No 1 (2026): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v14i1.2987

Abstract

Sensorless Brushless Direct Current (BLDC) motors are widely used in industrial applications due to their high efficiency and low maintenance requirements. However, commutation based on Back-Electromotive Force (Back-EMF) zero-crossing detection is highly susceptible to noise, leading to commutation timing inaccuracies. This paper proposes an improvement in sensorless BLDC motor commutation performance through the application of a Finite Impulse Response (FIR) digital filter to the Back-EMF detection signal. The FIR filter is designed to attenuate high-frequency harmonic components without compromising system stability. The proposed method is implemented on a three-phase inverter system employing six-step commutation controlled by a microcontroller. Simulation results indicate that the dominant noise frequency in the Back-EMF signal is reduced from 347.6 Hz to 212.5 Hz after filtering. Furthermore, hardware experimental results demonstrate a reduction in disturbance frequency from 317.23 Hz to 265.43 Hz. The application of the FIR filter improves the reliability of zero-crossing detection and enhances commutation timing accuracy compared to an unfiltered system. These results confirm that the proposed approach is effective in improving the commutation performance of sensorless BLDC motors based on Back-EMF detection.
Co-Authors A'yun, Rifki Qurotul Abdillah Aziz Muntashir Abdullah Aziz Muntashir Ade Rochmana Ade Rochmanu Aditya Ilham Pradana Adoe, Cenneth Paolo Anderson Bai Agustian, Singgih Ainur Rofiq Akbar, Gilang Ekavigo Astafil Alifian Nur Rahmadika Amrinsani, Farid Ananto Mukti Wibowo angga wahyu aditya Apriyanto, R. Akbar Nur APRIYANTO, RADEN AKBAR NUR Ardhia Wishnuprakasa Aries Alfian Prasetyo Arman Jaya Bambang Sumantri Bambang Sumantri Bambang Sumantri BASUKI, GAMAR Bayu Praharsena Dedid Cahya Happyanto Diah Septi Y. Dicky Rivaldo Ramdani Dimas Okky Anggriawan Eka Prasetyono, Eka Endro Wahjono Endro Wahjono, Endro Erawati, Fera Fachrurozy, Fachrurozy Fakhruddin, Hanif Hasyier Farid Amrinsani Farid Dwi Murdianto Fathur Zaini Rachman Fera Erawati Ferdiansyah, Indra Fifi Hesty Sholihah GAMAR BASUKI Gamar Basuki Gigih Prabowo Gigih Prabowo Gilang Rizki Saputra Hairul Faizi Hairulnizam Handri Toar HANIF HASYIER FAKHRUDDIN Hanif Hasyier Fakhruddin Hanif Hasyier FAkhruddin Hary Oktavianto Hendik Eko Hadi Suharyanto I Dewa Gede Hari Wisana Indhana Sudiharto Indra Ferdiansyah Intan Sholikha Jaya, Arma Jaya, Arma Kadek Reda Setiawan Suda Karisma Trinanda Putra, Karisma Trinanda Lucky Pradigta S.R. Makoto Chiba Margo P Mauridhi Heri Purnomo Mauridhi Heri Purnomo Mauridhi Hery Purnomo Mentari Putri Jati MOCHAMAD ARI BAGUS NUGROHO Mohammad Ashary Mohammad Ashary, Mohammad Mohammad Jauhari Mr. Sukamto Muhamad Milchan Muhammad Aditya Ardiansyah Muhammad Irfan Zaidan Muhammad Wahyudi Muna, M. Faza Zidnal Nibras Syarif Ramadhan Novie Ayub Windarko Novrian Eka Sandhi Nugroho, Syechu Dwitya Nur Yanti, Nur Nurwahidah Jamal Pradana, Aditya Ilham Pradigta S.R., Lucky Praharsena, Bayu Praharsena, Bayu Putu Agus Mahadi Putra Qudsi, Ony Asrarul R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Oktav Yama Hendra Raden Akbar Nur Apriyanto Ramadhan, Nibras Syarif Renny Rakhmawati, Safira Nur Hanifah, Renny Rakhmawati, Ridwan Ridwan Ridwan W.K. Rifqi Dary Suryanto Rochmana, Ade Rochmanu, Ade Rusli, Muhammad Rizani Safa Aulia Zerlina Saputra, Gilang Rizki SATO Yukihiko Septi Y., Diah Setiawan Suda, Kadek Reda Sindu Muhammad Imam Taufik Singgih Agustian Siswoyo, Charis Faridchie Soebagio Soebagio Sri Muntiah Andriami Subagio subagio Subagio Subagio Subagio Subagio, Subagio Suda, Kadek Reda Setiawan Sulis Wanto sutedjo Sutedjo Sutedjo Sutedjo Syahwir, Irawati Dewi Syamsul Arifin Syamsul Rohman Syechu Dwitya Nugraha Syechu Dwitya Nugroho Taufik, Sindu Muhammad Imam Taufiqurrahman Taufiqurrahman Utomo, Bedjo Wanto, Sulis Waras, Nandang Gunawan Tungga Wardhana, Dimas Aditya Putra Wildan Maulana Akbar Wishnuprakasa, Ardhia Wishnuprakasa, Ardhia wk, Ridwan wk, Ridwan Yeheskiel Rante Payung Yunanto, Bagus Yunanto, Prasetyo Wibowo Zahro Zachari Zerlina, Safa Aulia