Rancang Bangun Estimator Kecepatan Berbasis Adaptive Extended Kalman Filter (AEKF) Untuk Speed Sensorless Motor Induksi

Firdausy, Saarah Savira Mustika (2021) Rancang Bangun Estimator Kecepatan Berbasis Adaptive Extended Kalman Filter (AEKF) Untuk Speed Sensorless Motor Induksi. Undergraduate thesis, ITS.

[img] Text
02311640000090-Undergraduate_Thesis.pdf - Accepted Version
Restricted to Repository staff only until 1 April 2023.

Download (4MB) | Request a copy

Abstract

Seiring dengan meningkatnya emisi global tahunan yang hingga 2020 mencapai 36.48 MT CO2, penggunaan motor pembakaran internal mulai beralih menjadi motor listrik, salah satunya adalah motor induksi. Motor induksi dipilih dikarenakan tidak memerlukan brushes, inersia rotor yang rendah, ukuran dan berat yang ringan, serta relatif murah. Pada pengaplikasiannya, ketersediaan data informasi mengenai kecepatan sebuah motor induksi sangat diperlukan. Data kecepatan tersebut dapat diukur menggunakan sensor kecepatan, namun karena faktor cost serta faktor reliabilty sensor yang makin berkurang seiring berjalannya waktu maka dikembangkan sebuah metode speed sensorles motor induksi, salah satunya adanya algoritma Adaptive extended kalman filter, dengan masukan variabel tegangan dan arus dari motor induksi. Dalam tugas akhir ini dirancang sistem estimator tersebut menggunakan motor induksi 1.5kW 1400RPM, inverter siemens sinamics G120, sensor tegangan LV25-p, sensor arus H3A ACDC, DAQ NI USB 6001, dan software simulink matlab. Diperoleh hasil error estimasi pada kecepatan 200 RPM, 300 RPM, 500 RPM masing-masing adalah 1%, 3%, dan 1.3% serta dengan maksimum overshoot 45%, 32.6% dan 9.6%. ===================================================================================================== Along with the increase in annual global emissions which until 2020 reached 36.48 MT of CO2, the use of internal combustion motors has begun to switch to electric motors, one of which is an induction motor. The induction motor was chosen because it does not require brushes, low rotor inertia, light size and weight, and is relatively cheap. In its application, the availability of information data about the speed of an induction motor is needed. The speed data can be measured using a speed sensor, but due to the cost factor and the sensor reliability factor which decreases over time, a speed sensor method of induction motor was developed, one of which is the adaptive extended kalman filter algorithm, with variable voltage input and current from an induction motor. In this final project, the estimator system is designed using a 1.5kW 1400 RPM induction motor, a siemens sinamics G120 inverter, an LV25-p voltage sensor, an H3A ACDC current sensor, a DAQ NI USB 6001, and a matlab simulink software. The results of the estimation error at speeds of 200 RPM, 300 RPM, 500 RPM are 1%, 3% and 1.3%, respectively.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Speed sensorless, Motor Induksi, Estimator, AEKF
Subjects: Q Science > QA Mathematics > QA402.3 Kalman filtering.
T Technology > TA Engineering (General). Civil engineering (General) > TA593.35 Instruments, cameras, etc.
T Technology > TJ Mechanical engineering and machinery > TJ1058 Rotors
T Technology > TJ Mechanical engineering and machinery > TJ213 Automatic control.
T Technology > TJ Mechanical engineering and machinery > TJ217 Adaptive control systems
Divisions: Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User: Saarah Savira Mustika Firdausy
Date Deposited: 08 Mar 2021 21:10
Last Modified: 08 Mar 2021 21:10
URI: https://repository.its.ac.id/id/eprint/83817

Actions (login required)

View Item View Item