Perancangan Particle Filter Untuk Speed Sensorless Induction Motor di PT. INKA

Firdausi, Dinda Cahya (2020) Perancangan Particle Filter Untuk Speed Sensorless Induction Motor di PT. INKA. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penggerak utama yang biasanya digunakan pada industri kereta listrik yaitu motor induksi. Pada penelitian ini dikembangkan suatu metode mengenai sistem kerja motor induksi tanpa sensor (speed sensorless induction motor) dengan menggunakan particle filter (PF) yang mampu mengestimasi kecepatan dan mengetahui performansi estimator kecepatan berbasis PF. Tahapan yang dilakukan adalah pemodelan motor induksi, perancangan dan pengujian PF. Parameter PF yang dibutuhkan antara lain jumlah partikel (N), initial guess of state value (xo), kovarian awal (Po), kovarian noise sistem (Q), serta matriks kovarian noise pengukuran (R). Pada penelitian ini validasi model dilakukan dengan mengacu saat motor induksi beroperasi open loop. Berdasarkan hasil pemodelan, dimana dalam kondisi steady didapatkan nilai arus sebesar 4,41 A, nilai fluks sebesar 1,137 Wb, dan kecepatan sebesar 157,07 rad/s. Sedangkan dari perancangan PF dengan jumlah partikel sebanyak 250 didapatkan nilai RMSE paling kecil, dimana dalam kondisi steady didapatkan nilai arus sebesar 4,7 A, nilai fluks sebesar 1,142 Wb, dan kecepatan sebesar 157,08 rad/s. Setelah itu dilakukan pengujian dengan variasi beban, dimana nilai RMSE kecepatan semakin meningkat ketika beban yang diberikan semakin besar. Perbandingan performansi estimasi kecepatan dengan PF maupun EKF didapatkan melalui nilai RMSE. Dalam keadaan motor induksi tanpa beban performansi PF lebih baik dibandingkan EKF. Namun, dalam keadaan motor induksi ada beban performansi EKF lebih baik dibandingkan PF. Selain itu, kecepatan estimasi dari PF maupun EKF dapat menyesuaikan dengan perubahan tegangan yang ada.
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The main driving force that is usually used in the electric train industry is induction motors. In this research, a method was developing called speed sensorless induction motor with particle filter (PF) that able to estimate the speed and determine the performance of the speed estimator based on the PF. The steps taken are induction motor modeling, design and testing of PF. The required PF parameters include the number of particles (N), initial guess of state value (xo), initial covariance (Po), process noise vector covariance (Q), and measurement noise vector covariance matrix (R). In this research, model validation is done by referring to the induction motor operating open loop. Based on the results of modeling, wherein steady conditions are obtained a current value of 4.41 A, a flux value of 1.137 Wb, and a speed of 157.07 rad /s. While from the estimated PF with 250 particles obtained the smallest RMSE value, wherein steady conditions a current value of 4.7 A, flux value of 1.142 Wb, and speed of 157.08 rad/s. After that, the test is done with a variation of the load, where RMSE of speed increases when the load is greater. A comparison of speed estimation performance with PF and EKF is obtained through RMSE values. In a state of induction motor without load, the performance of PF is better than EKF. However, in the state of an induction motor when there is a load of the performance of EKF better than PF. Furthermore, the estimated speed of the PF and EKF can adjust to changes in the existing voltage.

Item Type: Thesis (Other)
Additional Information: RSF 621.313 6 Fir p-1 2020
Uncontrolled Keywords: Motor induksi, speed sensorless, particle filter
Subjects: T Technology > TF Railroad engineering and operation > TF872 Rails (Track)
T Technology > TJ Mechanical engineering and machinery > TJ1058 Rotors
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction.
Divisions: Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User: Dinda Cahya Firdausi
Date Deposited: 10 Jan 2024 07:25
Last Modified: 10 Jan 2024 07:25
URI: http://repository.its.ac.id/id/eprint/74513

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