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Jurnal Mahasiswa TEUB
Published by Universitas Brawijaya
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Search results for , issue "Vol. 11 No. 2 (2023)" : 25 Documents clear
PERANCANGAN PARAMETER PI-DIGITAL DENGAN METODE RST-POLE PLACEMENT PADA DIRECT TORQUE CONTROL MOTOR TRAKSI INDUKSI TIGA FASA Nur Hidayatus Safitri; Moch. Rusli; Bambang Siswojo
Jurnal Mahasiswa TEUB Vol. 11 No. 2 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

In the current development of industrial technology, electricity has been used as the prime mover. One of them is an electric motor, the most widely used is an induction motor. This is because induction motors have advantages in easier maintenance, relatively affordable prices, sturdy construction, and more reliable performance. Examples of the use of induction motors are in modes of transportation, both land, sea and air. However, induction motors still have a weakness which lies in the speed of themotor which is complicated to control. So the speed control research was carried out using the Direct Torque Control (DTC) method. To set the condition of the inverter switching using Space Vector Modulation (SVM). Speed, torque and flux control is carried out with a PI controller, where the PI parameters are determined using the RST-pole placement tuning method. Pole determination is based on the desired design performance criteria. The results of PI parameter on speed, torque, and flux arerespectively Kpω = 1277,59 and Tiω = -0,96, KpT = 8,4 and TiT = -0,2, Kpψ = 67,7 and Tiψ = 0,17. After testing, under no-load condition and a set point of 157 rad/s the system was able to reach a steady state with a steady state error of 0,6% within a settling time of 0,25 seconds. Under conditions of variation in load of 100 Nm, 200 Nm, 300 Nm, the system was able to reach steady state with steady state errors of 0,6%, 0,9%, and 1,2% in settling times of 0,25 seconds, 0,27 seconds, and 0,28 seconds, respectively. Keywords: Three Phase Induction Traction Motor, Direct Torque Control, Space Vector Modulation, PI-Digital Controller, RST Controller. DAFTAR PUSTAKA[1] S. E. Nugroho, W. Aribow, Ibrohim and A. C. Hermawan, "SISTEM PENGENDALIAN KECEPATAN MOTOR TIGA FASA MENGGUNAKAN METODE DIRECT TORQUE CONTROL 8 (DTC)," Jurnal Teknik Elektro, vol. X, no. 1, pp. 81-90, 2021.[2] K. R. S. Suda, E. Purwanto, B. Sumantri, H. H. Fakhruddin, A. A. Muntashir and M. R. Rusli, "PENGATURAN KECEPATAN MOTOR INDUKSI 3 FASA DENGAN MENGGUNAKAN PEMODELAN SISTEM (DTC) DIRECT TORQUE CONTROL," Jurnal Pendidikan Teknologidan Kejuruan, vol. XVIII, no. 2, pp. 237-248, Juli 2021.[3] A. Poorfakhraei, M. Narimani and A. Emadi, "A Review of Modulation and Control Techniques for Multilevel Inverters in Traction Applications," IEEE Access, Hamilton, 2021. [4] I. N. W. Satiawan, I. B. F. Citarsa and Supriono, "Perbandingan Kinerja Teknik Modulasi Inverter Dua-Level untuk Pengaturan Kecepatan Motor Induksi TigaFase," elektronik Jurnal Arus Elektro Indonesia, 2015.[5] Y. Satyanarayana and A. Srujana, "Speed Control of Induction Motor using Fuzzy PI Controller Based on Space Vector PulseWidth Modulation," International Journal Of Computational Engineering Research, pp. 1203-1209, September 2012.[6] I. D. Landau, "The R-S-T digital controller design and applications," Control Engineering Practice, pp. 155-165, 1998. [7] O. C. Sekhar, S. Lakhimsetty and A. H. Bhat, "A Comparative Experimental Analysis of Fractional Order PI Controller Based Direct Torque Control Scheme for Induction Motor Drive," International Transactions on Electrical Energy Systems, pp. 1-19, 2020.[8] R. Garg, P. Mahajan, N. Gupta and H. Saroa, "A Comparative Study between Field Oriented Control and Direct Torque Control of AC Traction Motor," in IEEE International Conference on Recent Advances and Innovations in Engineering, Jaipur, 2014.[9] M. Yusuf, V. Prasetia, S. D. Riyanto and A. A. Rafiq, "Desain Simulasi Sistem Pengaturan Motor Induksi Tiga Fasa dengan Switching Space Vector Pulse Width Modulation," ECOTIPE, vol. VI, no. 1, pp. 24-31, 2019.[10] D. w. Saputra, Design KOntrler PI Digital Berbasis Kriteria Integral Error Kuadratik pada Sistem Kontrol Kecepatan Motor DC,Malang: Skripsi, 2017.[11] I. D. Landau and G. Zito, DIGITAL CONTROL SYSTEMS : Identification, design and implementation, Berlin: Springer, 2006.[12] N. Pimkumwong and M.-S. Wang, "Direct Torque Control of Three-Phase Induction Motor based on Constant Voltage perFrequency Control with Simple Controller," in 15th International Conference on Electrical Engineering/Electronics, Computer,Telecommunications and Information Technology (ECTI-NCON2018), Taiwan, 2018.[13] S. Enache, A. Campeanu, I. Vlad, R. Zlatian and M. A. Enache, "Dynamic Analysis of New Induction Motor for Electrical Traction," in International Symposium on Power Electronics, Electrical Drives, Automation and Motion, Craiova, 2020.[14] K. L. Shi, T. F. Chan, Y. K. Wong and S. L. Ho, "Modelling and Simulation of Direct Self-Control Systems," in Int. J. Engng Ed,Kowloon, 2003.[15] S. Enache, A. Campeanu, I. Vlad and M. A. Enache, "Particular Dynamic States of Railway Traction Asynchronous Motors,"in The XIth International Symposium on Advanced Topics in Electrical Engineering, Bucharest, 2019.[16] N. R. Mulyawan, S. Yahya and A. R. AlTahtawi, "Pemodelan Kecepatan Motor Induksi Tiga Fasa Dengan Metode Proportional Integral Anti Wind Up (PiAw)," in Prosiding SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan), Sukabumi, 2020.
INVESTIGASI VARIASI LEBAR HISTERESIS PADA CONTROLEER ON-OFF TERHADAP PENURUNAN RIPPLE MOTOR TRAKSI KERETA CEPAT Nur Apriyana Putri; Moch. Rusli; Rini Nur Hasanah
Jurnal Mahasiswa TEUB Vol. 11 No. 2 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

The propulsion system for the fast train in this study uses a three-phase induction motor.The weakness of the induction motor lies in speed control.By using the direct torque control (DTC) method it can be overcome, but this method causes ripples in the output. Therefore, in the DTC method there is an on-of controller which will be set to suppress the ripple value. Setting is done on the value of the on-of switch contained in the flux and torque hysteresis controller to determine the ef ect of varying the hysteresis width on the on-of controller on decreasing ripple. In addition, the execution time is also added to the simulation series in order to determine the ef ect on the DTC method.The result of this study is that the ripple percentage value can be reduced by setting the flux hysteresis controller, which is from 18.238% to 6.330%. Then, with the settings on the torque hysteresis controller can also be reduced, which is from 6.330% to 6.231%. While the addition of execution time on the simulation, the ef ect is directly proportional to the ripple width of the output torque graph. Keywords: three phase induction motor, ripple, direct torque control (DTC), on-of controlle DAFTAR PUSTAKA[1] Maspriyanto, et al. (2010), “Pengaturan Kecepatan Motor Induksi 3θ Menggunakan Kontrol PI Berbasis Direct Torque Control”, hlm 1-2, Surabaya. [2] J. Siniaga, et al. (2021), “Kinerja Pengereman Motor Induksi Tiga Fasa”, Vol. 10, hlm 114- 119, Medan. [3] Nugroho, et al. (2021), “Sistem Pengendalian Kecepatan Motor Tiga Fasa Menggunakan Metode Direct Torque Control”, Vol. 10, hlm 81-90, Surabaya. [4] Koshy, Melvin. (2014), “Direct Torque Control Schemes for Induction Motor”, hlm 10-13, Trivandum. [5] Enache, et al. (2019), “Particular Dynamic States of Railway Traction Asynchronous Motors”, Bucharest, Romania. [6] Zulfatman, (2006), “Desain Pengendalian Kecepatan Motor Induksi 3 Phase Dengan PID Kontroler”, Vol. 1, hlm 144-154, Malang. [7] Rusli, et al. (2020), “Synthesis-Algorithm Of Bang-bang Controller With Delayed Feedback On Temperature Controller Systems”, IEEE. [8] Rahmani, et al. (2014), “Fuzzy Logic Controller and Cascade Inverter for Direct Torque Control of IM”, London. [9] Obed, et al. (2018), “Reduction of Ripple for Direct Torque Controlled Three Phase Induction Motor Based on A Predictive Control Technique”, Vol. 22, Iraq
DESAIN PI DENGAN FEEDFORWARD CONTROLLER PADA MOTOR TRAKSI GUNA PENGURANGAN PENGARUH DISTURBANSI Aprilia Dwi Setyawati; Moch. Rusli; Rini Nur Hasanah
Jurnal Mahasiswa TEUB Vol. 11 No. 2 (2023)
Publisher : Jurnal Mahasiswa TEUB

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Abstract

In this study, an analysis was carried out on the design of the PI controller and feedforward control to reduce the influence of interference due to load torque. Feedforward control works by detecting disturbance or called interference then the magnitude of interference will be compensated by the feedforward controller so that interference is reduced and does not interfere with the output value set by the set point. The determination of Kp and Ki controller parameters is carried out using the optimum symmetrical method and the value of the controller parameters Kp = 11.752 and Ki = 0.42 is obtained. Keywords: Three phase induction motor, PI controller, feedforward control, symmetrical optimum Daftar Pustaka[1] Leonhard W., Control of Electrical Drives, Springer-Verlag, Berlin Heiderberg. New York Tokyo. 1985.[2] Smith, Carlos A., and Armando B. Corripio. 1997. Principles and Practice of Automatic Process Control, 2nd Edition. New York:John Wiley & Sons, Inc,.[3] Enache, S., Campeanu, A., Vlad, I. and Enache, M.A., 2019, March. Particular Dynamic States of Railway Traction Asynchronous Motors. In 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE) (pp. 1-5). IEEE.[4] Wahab, H.A. and Sanusi, H., 2008. Simulink model of direct torque control of induction machine. American Journal of AppliedSciences, 5(8), pp.1083-1090.[5] Imantaka, Christoper. 2016. Perancangan PI Kontroler Pada Kontrol Kecepatan Motor DC Dengan Kombinasi Pole Placement Dan Symmetrical Optimum. Malang: Skripsi Teknik Elektro Universitas Brawijaya Malang.[6] Baskoro, F. and Nugroho, S.E., 2021. Sistem Pengendalian Kecepatan Motor Tiga Fasa Menggunakan Metode Direct TorqueControl (DTC). Jurnal Teknik Elektro, 10(1), pp.81-89.
KLASIFIKASI ALZHEIMER PADA CITRA MRI OTAK DENGAN CONVOLUTIONAL NEURAL NETWORK Muhammad Rafi’ Zaidan Maajid; Panca Mudjirahardjo; Akhmad Zainuri
Jurnal Mahasiswa TEUB Vol. 11 No. 2 (2023)
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Abstract

In deep learning, Convolutional Neural Network (CNN) is an algorithm from Artificial Neural Network (ANN) which is generally used to analyze visual images. This algorithm can automatically extract important features from each image without human assistance, besides that the CNN algorithm is also more efficient than other neural network methods, especially in memory and complexity. In training, the algorithm will be given training data in the form of images that have been labeled so that the algorithm will be able to recognize the important characteristics of each of the labeled images. After the training stage, the trained algorithm will be given data validation in the form of an unlabeled image to be analyzed and classified. The algorithm will analyze the training and validation data for the specified number of epochs and provide information in the form of the level of accuracy of each epoch that is performed. Some that affect the level of accuracy include the type of optimizer, the pixel size of the input image, and the number of epochs. In this study, the CNN algorithm was used with a layer sequence made personally by the author. The research was conducted in a cloud-based Jupyter notebook environment called Google Colab. The dataset used in this study is the Alzheimer's MRI Preprocessed Dataset which can be accessed by the public on the Kaggle website. The dataset consists of 6400 brain MRI scan images which are divided into four classes, namely: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. As much as 20% of the dataset is used as data validation. In this study, the dataset will be analyzed by the CNN algorithm with several predetermined scenarios, then the accuracy of the training and validation data will be compared with each other to find the most optimal scenario. There are two input image pixel size scenarios to be compared, namely 128 x 128 pixels and 224 x 224 pixels. There are three types of optimizers that will be compared, namely Stochastic Gradient Descent (SGD), Adam, and RMSprop. From the research results, the most optimal type of optimizer to use with the architecture that has been made and the Alzheimer's MRI Preprocessed Dataset is the Adam optimizer. Architectural training with an input size scenario of 224 x 224 pixels, seven epochs, and using the Adam optimizer achieves the most optimal accuracy rate, namely with a training data accuracy rate of 93.01% and a data validation accuracy rate of 94.45%. Architecture training with an input size scenario of 224 x 224 pixels and using the Adam optimizer achieves the most optimal number of epochs, namely achieving an accuracy level above 90% in just five epochs. Keywords: CNN, Alzheimer's, accuracy, optimizer, optimal. Daftar Pustaka [1] Burns, A., & Iliffe, S. (2009). Alzheimer's disease. Bmj-British Medical Journal, 338. [2] Dementia. (2022, 20 September). https://www.who.int/news-room/factsheets/detail/dementia [3] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. [4] Khan, S., Barve, K. H., & Kumar, M. S. (2020). Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer’sdisease. Current Neuropharmacology, 18(11), 1106-1125. [5] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. [6] Mendez, M. F. (2006). The accurate diagnosis of early-onset dementia. The International Journal of Psychiatry in Medicine, 36(4), 401-412. [7] Mortimer, J. A., Borenstein, A. R., Gosche, K. M., & Snowdon, D. A. (2005). Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression. Journal of geriatric psychiatry and neurology, 18(4), 218-223. [8] National Institute for Health and Clinical Excellence. (2006, November). Dementia: Quick Reference Guide. Diambil kembali darihttps://web.archive.org/web/20080227161412/http://www.nice.org.uk/nicemedia/pdf/CG042quickrefguide.pdf. [9] Simon, R. P., Aminoff, M. J., & Greenberg, D. A. (2009). Clinical neurology. Lange Medical Books/McGraw-Hill. [10] Smith, M. A. (1998). Alzheimer disease. International review of neurobiology, 42, 1-54. [11] Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and computers in simulation, 177, 232-243.
PERANCANGAN SISTEM BACKUP DAYA DAN TELEMONITORING DATA DENGAN PROTOKOL MQTT PADA SMART INKUBATOR BAYI Geraldio Ramadhan Safitri; n/a Rahmadwati; Moch. Rusli
Jurnal Mahasiswa TEUB Vol. 11 No. 2 (2023)
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Abstract

Indonesia is ranked the fifth country with the world’s most preterm babies. Therefore, the demand for infant incubators remains high. However, most infant incubators today still have some drawbacks, namely, the incubator relies on a grid power source. If a power outage happens, the incubator cannot be used. This research aims to solve the issue by designing a backup system for the incubator integrated with the Internet of Things (IoT) concept. The proposed system consists of an Automatic Transfer Switch (ATS) system, an automatic charging system, and a telemetry unit that can transmit incubator status data to a mobile application via the MQTT protocol. The test result shows that the designed ATS system can switch sources with a delay of 1.085 seconds for the PLN grid – inverter and 0.245 seconds for the inverter – PLN grid. The automatic charging system successfully recharges the Valve Regulated Lead Acid (VRLA) battery using a 3-stage charging method. Furthermore, the designed system can send incubator status data via MQTT protocol to mobile applications with a delivery time of 0.110 seconds. The designed MQTT topology has high scalability proven by the test, that it can connect with up to 1000 clients on one topic. Index Terms—Infant Incubator, Backup Power System, MQTT DAFTAR PUSTAKA[1] World Health Organization, “Born too soon,” Neuroendocrinol. Lett., vol. 25, no. SUPPL. 1, pp. 133–136, 2012, doi: 10.2307/3965140.[2] M. Ali, M. Abdelwahab, S. Awadekreim, and S. Abdalla, “Development of a Monitoring and Control System of Infant Incubator,” 2018. doi: 10.1109/ICCCEEE.2018.8515785.[3] PT PLN (Persero), “PT PLN in Number 2021 (Statistik PLN 2021),” pp. 1–102, 2021, [Online]. Available: https://web.pln.co.id/statics/uploads/2022/08/StatistikPLN-2021-29-7-22-Final.pdf[4] V. Hall, E. Geise, and N. H. Kashou, “The IncuLight: Solar-powered infant incubator,” 2014. doi: 10.1109/GHTC.2014.6970285.[5] I. Roihan, K. Tjandaputra A., E. A. Setiawan, and R. A. Koestoer, “Installing and testing the grashof portable incubator powered using the solar box ‘becare’ for remote areas without electricity,” Evergreen, vol. 7, no. 4, 2020, doi: 10.5109/4150516.[6] M. Syahid, D. Irianto, E. Sunarno, and S. St, “Rancang Bangun Charger Baterai dan Automatic Transfer Switch ( ATS ) Panel Surya – PLN Untuk Sumber Daya Tempat Sampah Otomatis,” J. Elektro PENS, vol. 2, no. 2, 2014.[7] I. A. Lazuardi, I. W. Farid, and C. W. Priananda, “Automatic Transfer Switch Dilengkapi Fitur Monitoring Website pada On-Grid Solar Home System,” J. Tek. ITS, vol. 10, no. 2, 2021, doi: 10.12962/j23373539.v10i2.68713.[8] I. Sukma et al., “Real-time wireless temperature measurement system of infant incubator,” Int. J. Electr. Comput. Eng., vol. 13, no. 1, pp. 1152–1160, 2022, doi: 10.11591/ijece.v13i1.pp1152-1160.

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