Rancang Bangun Real-Time Estimator Untuk Kecepatan Berbasis Extended Kalman Filter Pada Motor Induksi

Vanessa, Widya Lubis (2021) Rancang Bangun Real-Time Estimator Untuk Kecepatan Berbasis Extended Kalman Filter Pada Motor Induksi. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Motor induksi adalah salah satu jenis motor listrik arus bolak balik (AC) yang digunakan di industri. Dalam menjalankan sistem ini, diperlukan informasi kecepatan yang berasal dari sensor kecepatan. Namun penggunaan sensor kecepatan memiliki beberapa kekurangan. Oleh karena itu dilakukan penelitian mengenai real-time estimator berbasis Extended Kalman Filter (EKF). Pada tugas akhir rancang bangun ini, dibutuhkan komponen-komponen yaitu motor induksi, sensor arus, sensor kecepatan, arduino, Data Acquisition (DAQ) National Instrumen, toshiba inverter, dan software Simulink. Berdasarkan hasil pengujian, real-time estimator ini dapat melakukan estimasi kecepatan secara tepat di kecepatan 450, 500, 550, 600, dan 650 RPM dengan masing-masing RMSE steady statenya adalah 4.984%, 2.574%, 1.843%, 1.084%, dan 0.637%. Semakin tinggi kecepatan motor induksi, maka semakin dekat hasil estimasi dan pengukuran kecepatannya. Estimator ini membutuhkan waktu rata-rata 0.168 detik di Simulink untuk mencapai kecepatan estimasi steady state. Apabila terjadi perubahan kecepatan motor induksi, maka real-time estimator dapat mengestimasi kecepatan yang sesuai namun didahului adanya overshoot akibat dari perbedaan sudut antara arus dan tegangannya. ================================================================================================ Induction motor is one type of alternating current (AC) electric motor used in industry. In application of system, speed information is needed from the speed sensor. However, the speed of using the sensor has several drawbacks. Therefore, a research on real-time estimator based on Extended Kalman Filter (EKF) was conducted. In this final design project, components are needed, namely induction motor, current sensor, speed sensor, arduino, Data Acquisition (DAQ) National Instrument, Toshiba Inverter, and Simulink MATLAB software. Based on the test results, the EKF estimator can accurately estimate the speed at speeds of 450, 500, 550, 600, and 650 RPM with the steady state RMSE of 4.984%, 2.574%, 1.843%, 1.084%, and 0.637%. The higher the speed of the induction motor, the closer the value of estimation and measurement speed. This estimator takes an average 0.168 seconds in Simulink to reach steady state estimation speed. If any change in the speed of the induction motor, the real-time estimator can estimate the appropriate speed, but it is preceded by an overshoot due to the difference angular between the current and the voltage.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Kecepatan, Extended Kalman Filter, Motor Induksi, Real-time Estimator, Induction Motor, Real-time Estimation, Speed Estimation
Subjects: Q Science > QA Mathematics > QA402.3 Kalman filtering.
T Technology > TF Railroad engineering and operation > TF1327.O58 High speed ground transportation (train)
T Technology > TF Railroad engineering and operation > TF193 Estimates, costs, etc.
T Technology > TJ Mechanical engineering and machinery > TJ1058 Rotors
T Technology > TJ Mechanical engineering and machinery > TJ213 Automatic control.
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction.
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7878 Electronic instruments
T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL445.M369 Motorcycles--Electric equipment--Maintenance and repair.
Divisions: Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User: Widya Lubis Vanessa
Date Deposited: 17 Aug 2021 14:55
Last Modified: 17 Aug 2021 14:55
URI: https://repository.its.ac.id/id/eprint/87108

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