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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 | 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.
Kendali Kecepatan Motor Induksi 3 Fase Berbasis Particle Swarm Optimization (PSO) HANIF HASYIER FAKHRUDDIN; HANDRI TOAR; ERA PURWANTO; HARY OKTAVIANTO; RADEN AKBAR NUR APRIYANTO; ANGGA WAHYU ADITYA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3 (2020): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektro
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i3.477

Abstract

ABSTRAKMotor induksi secara struktur dan kendali standarnya dirancang untuk bekerja pada kecepatan nominal, sehingga sulit mengendalikan kecepatan sesuai kebutuhan karena akan mengubah konstruksi motor. Penelitian tentang pengendalian motor induksi agar semudah mengendalikan motor DC sudah banyak dilakukan oleh peneliti, salah satunya adalah dengan kendali skalar. Kendali skalar banyak digunakan karena memiliki keunggulan sederhana, biaya murah, mudah didesain dan diimplementasikan, serta yang paling penting tidak memerlukan parameter dari motor induksi. Penggunaan kendali skalar yang telah dilengkapi pengendali PID penalaan otomatis, dengan parameter yang telah dioptimalkan algoritma Particle Swarm Optimization (PSO), akan memudahkan pengendalian kecepatan motor induksi tiga fase pada kecepatan beragam. Simulasi penalaan otomatis PID menggunakan PSO telah dilakukan dengan LabView, dengan karakteristik maksimal 10% overshoot, 1% error steady state dan rise time kurang dari 2 milidetik. Sementara dalam pengujian real time dengan MyRIO hasilnya tanpa overshoot, 5.5% error steady state maksimal dan rise time maksimal 5 detik.Kata kunci: Kendali skalar, PID, Particle Swarm Optimization, LabView ABSTRACTInduction motor is designed at nominal speed as default, we have to change its stucture to obtain dessired speed. Many researchers developt method how to control induction motor as simple as DC motor, one of the methods is scalar control. Scalar control has several benefits, such as simply, low cost, easily designed and implemented, and the main banefit is no necessary motor parameters. Using scalar control with PID controller that optimized Partical Swarm Optimization (PSO) algoritm, will ease to control 3 phase induction motor variant speed. Simulation auto tunning using PSO has done on LabView, it has some characteristic, they are 10% overshoot, 1% steady state error, and rise time within 2ms. In other hand, real time test using MyRIO got no overshoot, 5.5% steady state error maximal, and rise time maximal 5 s characteristic.Keywords: Scalar control, PID, Particle Swarm Optimization, LabView
Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung HANIF HASYIER FAKHRUDDIN; HANDRI TOAR; ERA PURWANTO; HARY OKTAVIANTO; GAMAR BASUKI; RADEN AKBAR NUR APRIYANTO; ABDILLAH AZIZ MUNTASHIR
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 4 (2021): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektro
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i4.786

Abstract

ABSTRAKKendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk meningkatkan keandalan pada berbagai kecepatan acuan. Metode cerdas Particle Swarm Optimization (PSO) digunakan untuk optimasi dataset ANFIS. Pengujian keandalan dilakukan dengan membandingkan PID konvensional dengan PID-ANFIS pada motor induksi 3 fase berdaya 2HP. Validasi penelitian dilakukan melalui simulasi di platform LabView. PID-ANFIS membuktikan hasil yang jauh lebih baik dari kendali PID konvensional pada berbagai kecepatan acuan. Pemilihan rise time tercepat sebagai fungsi fitness menghasilkan kendali yang memiliki dead time dan rise time 1.5x lebih cepat. PID-ANFIS berhasil menghilangkan undershoot dan osilasi steady state ketika uji kecepatan tinggi.Kata kunci: Kendali Vektor, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView ABSTRACTVector control is the best solution in induction motor control to enhance its dynamic character and efficiency. In this research, a PID speed controller is combined with the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance reliability at various reference speeds. The intelligent method Particle Swarm Optimization (PSO) is used to optimize the ANFIS dataset. Reliability testing is done by comparing conventional PID with PID-ANFIS on a 2HP 3-phase induction motor. The research validation was carried out through a simulation on the LabView platform. The PID-ANFIS proved significantly better results than conventional PID control at a wide range of reference speeds. Selection of the fastest rise time as a fitness function results in a control that has a dead time and a rise time of 1.5x faster. PID-ANFIS successfully negates undershoot and steadystate oscillations during high-speed tests.Keywords: Vector Control, Adaptive Neuro Fuzzy Inference System, Particle Swarm Optimization, LabView
Stator Flux Estimator Using Feed-Forward Neural Network for Evaluating Hysteresis Loss Curve in Three Phase Induction Motor Bayu Praharsena; Era Purwanto; Arma Jaya; Muhammad Rizani Rusli; Handri Toar; Ridwan wk
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.
Rancang Bangun Modul Praktikum Motor AC dengan Aplikasi Pengaturan Posisi dengan Menggunakan PID Nurfaizah M; Didi Istardi; Handri Toar
JURNAL INTEGRASI Vol 7 No 1 (2015): Jurnal Integrasi - April 2015
Publisher : Politeknik Negeri Batam

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

Abstract

Penggunaan motor AC 3 fasa saat ini banyak digunakan didunia industri untuk mencapai kecepatan putaran motor yang diinginkan, maka dibutuhkan sistem kendali kecepatan motor. Saat mengatur posisi sebuah motor AC 3 fasa sering terjadi over shoot dan setting time yang lama, untuk itu dibutuhkan sebuah metode pengontrolan yang dapat mengatasi kekurangan tersebut. Pada penelitian ini menggunakan metode kontrol PID untuk menghasilkan output yang konstan dan untuk mengurangi nilai error saat mengatur posisi motor. Keluaran dari PID selanjutnya di absolute kan untuk menghilangkan tegangan negatif yang dikeluarkan PID, kemudian keluaran dari absolute akan masuk ke inverter Altivar 312 agar motor dapat berputar kearah forward, reverse atau stop. Hasil percobaan menunjukkan motor dapat berputar sesuai dengan set point dengan rata-rata error terbesar 2.6 %. Pengotrolan posisi motor AC memiliki tingkat keberhasilan sebesar 80 %. Kesalahan pembacaan posisi disebabkan karena terjadi kerusakan pada potensiometer.
Visualisasi 3Dimensi untuk Memperkaya Pengoperasian Jarak Jauh dengan Mengunakan Kamera Webcam Daniel Sutopo Pamungkas; Handri Toar
JURNAL INTEGRASI Vol 9 No 1 (2017): Jurnal Integrasi - April 2017
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v9i1.264

Abstract

Pada pengendalian robot jarak jauh, dibutuhkan informasi mengenai sekitar dari robot. Informasi ini mambuat operator dapat mengendalikan robotnya dengan lebih baik. Informasi visual adalah informasi yang paling banyak digunakan oleh sistem pengoperasian robot jarak jauh. Informasi visual yang digunakan sebagian besar masih menggunakan informasi dua dimensi. Robot menggunakan sebuah kamera dan operator melihat informasi dari sebuah layar monitor. Hal ini memiliki kekurangan antara lain operator tidak mendapatkan efek kedalaman sehingga operator memiliki kesulitn untuk mengira jarak antara robot dan objek didepannya. Kekurangan ini dapat diselesaikan dengan menggunakan sistem visual tiga dimensi. Namun sistem ini membutuhkan kamera stereo yang tidak murah. Penelitian ini meneliti sebuah sistem yang mengunakan dua buah webcam yang terhubung dengan sebuah komputer, dan operator dapat merasakan sensasi 3 dimensi dengan menggunakan sebuah Virtual reality headset Kamera-kamera ini diletakan pada dua motor steper sehingga digerakan keatas-kebawah serta samping kiri dan kanan. Kemampuan gerak ini membuat operator mendapat informasi mengenai keadaan sekeliling dari robot. Motor-motor ini dikendalikan dari headset sehingga memudahkan operator. Sistem ini diharapkan dapat digunakan pada robot yang dikendalikan jarak jauh sehingga operator dapat mengoperasikan robot lebih baik lagi.
METODE PENENTUAN RUGI-RUGI HISTERESIS PADA PENGATURAN MOTOR INDUKSI BERBASIS VECTOR CONTROL Novrian Eka Sandhi; Era Purwanto; Dedid Cahya Happyanto; Ridwan W.K.; Handri Toar
JURNAL INTEGRASI Vol 12 No 1 (2020): Jurnal Integrasi - April 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1171.186 KB) | DOI: 10.30871/ji.v12i1.1299

Abstract

Compared to direct current (DC) motors, the three-phase induction motors have several advantages such as: big torque, low maintenance cost, and rugged. For those reasons, induction motors are dependable as the prime mover in industrial and transportation sectors. In order to increase the performance of induction motors, a vector control based driving method had been deleveloped to operate the induction motors in various level of speed. Some manufacturers begin to use induction motors as the mover of 2 or 4-wheeled electric vehicles in city/urban transportation. Due to restricted capacity of battery as the power source, many researches on vector control are now focussed on advancing the driving scheme which in turn increasing mileage and lifetime of induction motors. One factor which supports that purpose is the evaluation of losses occurred during induction motor operation. During low speed operation, hysteresis loss as a consequence of stator core magnetization phenomenon takes a major part of overall losses. This research proposed a simple and applicable design of hysteresis loss determination on induction motor controlled by vector control scheme. The simulation using particular induction motor as a sample found that the iron loss PFE ranged between 2,55 x 10-8 to 1,09 x 103 Watt, the hysteresis loss Ph ranged between 2,07 x 10-8 to 5,15 x 102 Watt, and the hysteresis loss to iron loss rate ranged between 47,09 % to 81,18 %.
DESAIN KONTROL KECEPATAN MOTOR INDUKSI TIGA FASA MENGGUNAKAN FUZZY PID BERBASIS IDIRECT FIELD ORIENTED CONTROL Ridwan Ridwan; Era Purwanto; Hary Oktavianto; Muhammad Rizani Rusli; Handri Toar
JURNAL INTEGRASI Vol 11 No 2 (2019): Jurnal Integrasi - Oktober 2019
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1017.252 KB) | DOI: 10.30871/ji.v11i2.1356

Abstract

Motor induksi tiga fasa (MITF) umumnya digunakan di berbagai aplikasi di industri karena keandalannya, biaya rendah, kontruksi kokoh, perawatan rendah, dan effisiensi yang tinggi. Namun untuk mengontrol MITF tidak semudah seperti mengontrol motor DC, karena MITF merupakan motor yang tidak linear. Penggunaan metode indirect field oriented control (IFOC) dengan kontroler fuzzy proportional integrator and derivative (FPID) dipilih untuk dapat mengatur kecepatan MITF. Metode IFOC akan membuat MITF dapat dikontrol seperti motor DC penguat terpisah. Kontroler FPID yang di desain dengan mengganti kontroler PID konvensional. Performa kontroler FPID yang di desain dibandingkan dengan kontroler PID konvensional. Performa respon yang dibandingkan seperti rise time, settling time, overshoot, steady state error, dan undershoot. Hasil simulasi yang dibuat menunjukkan bahwa dengan menggunakan kontroler FPID lebih baik dibandingkan dengan kontroler PID. Dimana respon overshoot untuk kontroler FPID 0% sedangkan kontroler PID adalah 0.23%. Begitu pula dengan respon undershoot untuk kontrol FPID adalah 2.88% sedangkan kontroler PID adalah 6.78%. Untuk respon rise time, settling time, dan steady state error tidak jauh berbeda dari kedua kontroler. Sistem yang sudah di buat disimulasikan di platform LabView
PENALA PARAMETER PID OTOMATIS PADA PENGATUR KECEPATAN MOTOR INDUKSI TIGA FASA Handri Toar; Era Purwanto; Hary Oktavianto; Ridwan Ridwan; Muhammad Rizani Rusli
JURNAL INTEGRASI Vol 12 No 1 (2020): Jurnal Integrasi - April 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1277.092 KB) | DOI: 10.30871/ji.v12i1.1372

Abstract

PID is one of the simple controller, fast computing, easy to implement, reliable to face the disturbance especially to handle the linear system. Meanwhile induction motor is one of a high nonlinear system. Is PID can handle the induction motor. Therefore PID controller enhancement is needed. The controller has the ability to tune up the parameter while running. To tune-up, the controller needs a bunch of collections of parameters PID that ready to use. If we use the manual way to collect like trial and error, it will consume much power and time. And not all systems can be used the Ziegler-Nichols method. This research offering an algorithm for the autotuning PID parameter to control the speed of induction motor based on vector control to collect the PID parameter automatically. After validation by using LabVIEW simulation, the system provides a good speed response without overshoot when the speed increased and without undershoot when the speed decreased.
ALAT KENDALI KECEPATAN MOTOR PADA PENGGERAK DEPAN SEPEDA LISTRIK DI POLITEKNIK NEGERI BATAM Qoriatul Fitriyah; Renaldy Aritha; Handri Toar; Muhammad Prihadi Eko Wahyudi
JURNAL INTEGRASI Vol 12 No 2 (2020): Jurnal Integrasi - Oktober 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v12i2.2417

Abstract

Brushless DC motor is applied in many field of industries, one of which transportation sector. The operation of BLDC motor need a speed control system as commutation process since it does not applied any brush. This research using an Arduino nano as its microcontroller to regulate the switching driver mosfet with IR2103 type. This mosfet driver is used to trigger mosfet switching at iverter 3 phase circuit so that power from battery can flow to BLDC motor. Mosfet type is STP75NF75 with Vds 75 Volt and Ids 80 Amps, while type of motor used is BLDC 350 watt and 36 Volt. The BLDC motor has Hall Effect sensor as rotor positioning detection. As for the commutation process, PWM is used as pulse modulation which is represented the motor speed and frequency of PIN default of Arduino. Speed earned is 224-340 rpm with power consumption of 20-21 Watts.