İndüksiyon motorlarda yinelemeli YSA tabanlı durum kestirimi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Vektör kontrolü olarak da bilenen alan uyumlu kontrol, yüksek performanslı indüksiyon motor (İM) kontrolü için oldukça kullanışlı bir tekniktir. Alan uyumlu kontrollü sürücülerin kullanıldığı yüksek performanslı İM kontrolünde, rotor akısı, stator akısı ve rotor akımı gibi durum değişkenlerine ihtiyaç duyulur. Özellikle hız sensörsüz İM kontrolünde doğrudan ölçülemeyen rotor akısının kestirimi oldukça önemlidir. Yüksek performanslı kontrol için İM’nin ölçülemeyen durum değişkenlerinin kestiriminin yanı sıra parametre adaptasyonu veya değişen parametrelerinin kestirimi de önem arz etmektedir. Bu tez çalışmasında öncelikle durum değişkenlerini esas alan indüksiyon motorun dq eksen sistemi durum uzayı matematiksel modelleri düzenlenmiştir. Ardından yüksek performanslı alan uyumlu İM kontrolü için uygun durum uzay modellerinin kullanıldığı asimtotik gözlemleyicilere, KF ve GKF algoritmalarına ve Yapay Sinirsel Ağ (YSA) dayalı durum kestirim algoritmaları ayrıntılı olarak ele alınıp değişik çalışma koşulları için incelenmiştir. Özellikle dolaylı alan uyumlu kontrol için önem arz eden rotor akı bileşenlerinin kestirimi için Elman Yapay Sinirsel Ağ (EYSA) ve PI-EYSA’ya dayalı iki yeni kestirim algoritması önerilmiştir. Önerilen algoritmalar ve GKF algoritması değişik çalışma koşulları altında ve farklı dalga biçimli besleme gerilimleri için İM’den elde edilen benzetim ve deneysel çıkış ölçümlerine dayalı çevrim içi ve çevrim dışı olarak ayrı ayrı test edilmiştir. Geliştirilen kestirim algoritmaları ve GKF ile elde edilen kestirim sonuçları birbirleri ve gerçek sonuçlar ile karşılaştırılarak gerekli irdelemeler yapılmıştır.The field oriented control also known as the vector control is a useful highperformance technique to control an induction motor (IM). With high-performance control of IM are used field oriented controlled drives where there are needed state variables as rotor fluxes, stator fluxes and rotor currents to be known. In particular for speed sensorless IM control, estimation of the rotor fluxes that can not be measured directly is very important. For high-performance IM control, estimation of unmeasurable state variables as well as estimation of changing parameters or the parameter adaptation is also of great importance. In this thesis study, state variables of state space mathematical models of the induction motor based on d-q axis system has been organized primarily. After, asymtotic observers, Kalman Filter (KF) and Extended Kalman Filter (EKF) algorithms and Artificial Neural Network (ANN) algorithms based on the state estimation has been investigated for different operating conditions for the high performance field compatible IM control. To estimate the rotor flux components especially for indirect field oriented control there has been proposed two new estimation algorithms based on Elman Artificial Neural Network (ENN) and PIENN. Proposed algorithms and EKF algorithm has been tested separately with online and off-line simulational and experimental IM measurements based on under different working conditions with different waveformed supply voltages. For estimation and actual results obtained by the devoloped algorithms and EKF are compared with each other with making the necessary examinations

İndüksiyon motorun durum değişkenlerini kalman filtreleme algoritması ile kestirimi ve matlab simülasyonu

Bu tezin, veri tabanı üzerinden yayınlanma izni bulunmamaktadır.ÖZET Anahtar Kelimeler : İndüksiyon motor modeli, vektör kontrolü, karmaşık durum değişkenleri, gözlemleyici, Kalman filtresi, matlab, simulink. İndüksiyon motorun hız ve konum kontrol devrelerinde kullanılan algılayıcılarının azaltılması, düşük maliyetli ve yüksek güvenirliğe sahip indüksiyon motor sürücülerin ortaya çıkmasına sebep olunmuştur. İndüksiyon motor ile ilgili mevcut matematik modellerin, gelişen teknoloji ile indüksiyon motoru sürme tekniklerine uygulanabilmesi sonucu önemli sonuçlar elde edilmeye başlanmıştır. Bu modelleme ile indüksiyon motorun ölçülen gerilim ve akım değerleri yardımıyla, motorun anlık hız bilgisi, konum bilgisi, milin ucunda bulunan yük momenti ve indüksiyon motorunun rotorunda oluşan akı değerlerinin elde edilmesine olanak sağlanmıştır. Bu tez çalışmasında öncelikle değişik türden indüksiyon motor kontrol algoritmaları incelendi. Bu algoritmalardan v/f kontrol algoritması, vektör kontrol algoritması ve kalman filtreleme algoritmaları ele alınarak birbirlerine göre üstün ve zayıf yönleri irdelendi. Kalman filtreleme algoritması indüksiyon motor dq eksen sistemindeki Vas modeline uygulanarak, rotor hızı ve motor akı bileşenlerinin kestirimi amaçlanmıştır. Amaçlanan filtreleme algoritmasının giriş datalan stator akım ve gerilim bilgileridir. Filtre çıkışında ise rotor açısal hızı, rotor akı bileşenleri ve stator akım bileşenleri kestirilmektedir. Stator akım bileşeninin tekrar kestiriminin amacı, kestirim sonuçlan ile ölçüm sonuçlan karşılaştınlarak filtreleme algoritmasının performansının incelenmesidir. Söz konusu algoritmanın simülasyonu matlab-simulink ortamında gerçekleştirildi. Sinusoidal giriş gerilimi için değişik yük momentleri altoda elde edilen simülasyon sonuçlanndan önerilen kestirim algoritmasının oldukça doğru sonuçlar verdiği gözlendi. ıxPARAMETRE ESTIMATION OF INDUCTION MOTOR'S STATE VARIABLES WITH KALMAN ALGORITHM AND MATLAB SIMULATION SUMMARY Key words : Induction motor model, vector control, komglex state variables, observer, Kalman filter, matlab, simulink. This work focuses on observers estimating flux linkage and speed for induction machines. With speed estimation, sensorless control is possible, meaning that the speed of induction machines without mechanical speed sensors can be controlled. The observer based sensorless drive system has superior dynamic performance compared to a system with an open loop frequency inverter, yet it is neither more complex nor expensive. Using mechanical equivalent models of the induction machine and observers, an accurate flux observer working in the entire speed region of the induction machine is presented. The flux observer is expanded into a combined flux and speed observer, measuring only stator current and voltage. A method for sensorless control is proposed, analyzed and experimentally verified. Observer and controller calculations are performed by matlab simulink.

FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time

In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order physical system (buck converter) that was structured with power electronics elements on Simulink platform. Finally, the success of the control process was discussed by observing results

Realization of a digital chaotic oscillator by using a low cost microcontroller

This study addresses the in-detail steps to create a chaotic oscillator having continuous-time equations using a microcontroller hardware which has a lower clock-frequency and narrower data bus, as well as much lower hardware, software and algorithm development costs compared to chaotic oscillators developed using analog circuit components or a hardware-based software platform such as FPGA. For this purpose, a Lorenz chaotic oscillator with continuous-time nonlinear equations was selected. Lorenz t-domain equations were transformed into S-domain and Z-domain respectively. After these transformations, a detailed flowchart was given to illustrate the steps required to implement the chaotic oscillator in the microcontroller. All the details derived were simulated by running simultaneous MATLAB-SIMULINK simulations. And, the performance of the discrete-time chaotic oscillator executed in the PIC18F452 microcontroller produced by the Microchip Technology Inc. was visualized by 1D and 2D graphs on an oscilloscope screen

Recurrent Neural Network Based Nonlinear State Estimation for Induction Motors

This study presents a recurrent neural network (RNN) based nonlinear state estimator which uses an Elman neural network structure (ENN) for state estimation of a squirrelcage induction motor. Proposed algorithm only uses the measurements of the stator currents and the rotor angular speed, and learns of the dynamic behavior of the state observer from these measurements, through prediction error minimization. A squirrel-cage induction motor was fed from sinusoidal, six-steps, and Pulse Width Modulation (PWM) supply sources at different times in order to observe the performance of the proposed estimator for different operation conditions. Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and it performs better than Extended Kalman Filtering (EKE) in terms of accuracy and convergence speed. Copyright (C) 2011 Praise Worthy Prize S.r.l. - All rights reserved

PI Parameter Optimization By Fire Fly Algorithm For Optimal Controlling Of A Buck Converter's Output StateVariable

In this study, the buck converter circuit which is the topology of the power electronic circuit has been investigated to obtain the desired output voltage. The state variable of the circuit that is also used output of the system have been optimally controlled. The PI controller which is the basic structure and widely used in the practical applications has been run in discrete time. The PI controller parameters can be calculated by using a method based on the rootlocus, pole placement or Ziegler–Nichols method. However, these methods can not provide a solution with highe fficiency signal output voltage. In order to obtain the minimal cost value of the control process, Kp and Ki control parameters are optimized by using the Fire Fly (FF) algorithm based on swarm intelligence. The Matlab-Simulink program has been utilized for the simulations. The results show that the FF algorithm may observe the optimal values for the controller parameters

PI's Parameter Optimization Based On IWO For Optimal Controlling Of A Buck Converter's Output

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY -- IEEE Turkey Sect, Anatolian SciIn this study, the output value of a Buck Converter, which is one of the power electronics circuit topologies and used for efficient energy conduction, is controlled. The discrete time PI algorithm is used in the control process. Kp and Ki parameters of the controller are optimized in order to ensure that the control process is fault-free with the lowest cost and the reference DC voltage level. There was used Invasive Weed (IWO) for parameter optimization. Simultaneous operations of the control process were performed via Matlab-Simulink. The Success of control process were discussed by observing results.WOS:0004268687000732-s2.0-8503990339

SFLA based PI parameter optimization for optimal controlling of a Buck converter's voltage

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY -- IEEE Turkey Sect, Anatolian SciIn this work, it is aimed to control the output of Buck Converter which steps down a DC voltage level. Discrete Time PI Algorithm is chosen as control algorithm. The controller parameters of K-p and K-i is calculated by means of the optimization process to increase the efficiency of power transmission of Buck Converter instead of classical methods like Pole Placement Method. It is chosen Shuffled Frog Leaping Algorithm (SFLA) which is an iterative algorithm as optimization algorithm. The results obtained from simultaneous executions of control process is discussed by simulating in Matlab-Simulink.WOS:0004268687000722-s2.0-8503989657

Elman neural network-based nonlinear state estimation for induction motors

This study presents a recurrent neural network (RNN)-based nonlinear state estimator that uses an Elman neural network structure (ENN) for state estimation of a squirrel-cage induction motor. The proposed algorithm only uses the measurements of the stator currents and the rotor angular speed, and it learns the dynamic behavior of the state observer from these measurements through prediction error minimization. A squirrel-cage induction motor was fed from sinusoidal, 6-step, and pulse-width modulation (PWM) supply sources at different times in order to observe the performance of the proposed estimator for different operation conditions. Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy and convergence speed

Comparative Controlling of the Lorenz Chaotic System Using the SMC and APP Methods

The Lorenz chaotic system is based on a nonlinear behavior and this causes the system to be unstable. Therefore, two different controller models were developed and named as the adaptive pole placement and sliding mode control (SMC) methods for the establishment of continuous time nonlinear Lorenz chaotic system. In order to achieve this, an improved controller structure was developed first theoretically for both the controller methods and then tested practically using the numerical samples. During the establishment of adaptive pole placement method for the Lorenz chaotic system, various stages were applied. The nonlinear chaotic system was also linearized by means of Taylor Series expansion processes. In addition, the feedback matrix of the adaptive pole placement method was determined using linear Jacobian matrix. The chaotic system reached an equilibrium point by using both the SMC and adaptive pole placement methods; however the simulation results of the SMC had better success than adaptive pole placement control technique.WOS:0004537870000012-s2.0-8505893354