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2. Optimisation of Model Predictive Torque Control Strategy with Standard and Multi-Objective Genetic Algorithms

Author

Zerdali Emrah and Gurel Aycan

Subjects
electric drive system, induction motor, model predictive control, optimisation, genetic algorithm, Electronics, TK7800-8360
Abstract

In this paper, the flux error-related weighting factor (WF) of the predictive torque control (PTC) strategy for induction motor (IM) control is optimised by a standard genetic algorithm (SGA) through speed errors only and multi-objective genetic algorithm (MOGA) through torque and flux errors. This paper compares the performances of both optimisation methods. Compared to MOGA, SGA offers a straightforward way to select WF and does not need a decision-making method to choose a final solution. But MOGA considers the given problem in a multi-objective way and directly optimises the control objectives of the PTC strate-gy. Comparisons are made over the flux and torque ripples, total harmonic distortion of stator phase current, and average switching frequency for different operating conditions. Simulation results show that both methods choose a close WF value. Consequently, SGA stands out in the optimisation of the PTC strategy with its simple structure.

Published
2023
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5. The Effect of Different Decision-Making Methods on Multi-Objective Optimisation of Predictive Torque Control Strategy

Author

Gurel Aycan and Zerdali Emrah

Subjects
predictive torque control, induction motor, multi-objective optimisation, decision-making method, Electronics, TK7800-8360
Abstract

Today, a clear trend in electrification process has emerged in all areas to cope with carbon emissions. For this purpose, the widespread use of electric cars and wind energy conversion systems has increased the attention and importance of electric machines. To overcome limitations in mature control techniques, model predictive control (MPC) strategies have been proposed. Of these strategies, predictive torque control (PTC) has been well accepted in the control of electric machines. However, it suffers from the selection of weighting factors in the cost function. In this paper, the weighting factor associated with the flux error term is optimised by the non-dominated sorting genetic algorithm (NSGA-II) algorithm through torque and flux errors. The NSGA-II algorithm generates a set of optimal solutions called Pareto front solutions, and a possible solution must be selected from among the Pareto front solutions for use in the PTC strategy. Unlike the current literature, three decision-making methods are applied to the Pareto front solutions and the weighting factors selected by each method are tested under different operating conditions in terms of torque ripples, flux ripples, cur-rent harmonics and average switching frequencies. Finally, a decision-making method is recommended.

Published
2021
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7. Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

Author

Zerdali Emrah and Barut Murat

Subjects
Extended Kalman filter, Induction motor, Load torque and inertia estimation, Speed-sensorless control, Observability analysis, Electronics, TK7800-8360
Abstract

This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

Published
2018
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8. Uyarlamalı genişletilmiş Kalman filtresi-tabanlı sabit anahtarlama frekanslı hız-algılayıcısız doğrudan moment kontrollü sürücü sisteminin tasarımı

Author

İNAN, Remzi and ZERDALİ, Emrah

Subjects
Engineering, asenkron motor, lcsh:TA1-2040, Mühendislik, uyarlamalı genişletilmiş kalman filtresi, Asenkron motor,Uyarlamalı genişletilmiş Kalman filtresi,Sabit anahtarlama frekanslı doğrudan moment kontrolü, sabit anahtarlama frekanslı doğrudan moment kontrolü, lcsh:Engineering (General). Civil engineering (General)
Abstract

Bu çalışmada, hız-algılayıcısızasenkron motor (ASM) kontrolü için uyarlamalı genişletilmiş Kalman filtresi (UGKF) tabanlı sabit anahtarlama frekanslı doğrudan moment kontrollü (SAF-DMK’lı) sürücü sisteminin tasarımı gerçekleştirilmektedir. Stator akısı tabanlı ASM modelini kullanan UGKF ile GKF’lerin kestirim başarımını doğrudan etkileyen ve geleneksel GKF’lerde sabit olarak kabul edilen sistem gürültüsü kovaryans matrisi çalışma koşullarına göre çevrimiçi olarak güncellenmektedir. UGKF’nin geleneksel DMK’lı sürücü yerine, SAF-DMK’lı sürücüye dahil edilmesinin nedeni geleneksel DMK kontrolündeki moment dalgalanmalarının ve değişken anahtarlama frekansından kaynaklanan anahtarlama kayıplarının azaltılmasıdır. SAF-DMK’lı sürücünün ihtiyaç duyduğu stator akısının stator duran eksen takımı bileşenlerine ve rotor mekanik açısal hızına ek olarak, stator akımının stator duran eksen takımı bileşenleri ve yük momenti önerilen UGKF tabanlı gözlemleyici ile kestirilmektedir. Yük momenti kestirimi ile önerilen hız-algılayıcısız sürücü sisteminin bozucu yük momenti değişimlerine karşı dayanıklı olması sağlanmaktadır. Son olarak, önerilen UGKF-tabanlı hız-algılayıcısız SAF-DMK’lı sürücü sistemi benzetim çalışmaları altında doğrulanmaktadır.

Published
2020

18. Metaheuristic optimization of predictive torque control for induction motor control.

Author

Gürel, Aycan and Zerdali, Emrah

Subjects
*METAHEURISTIC algorithms, *TORQUE control, *INDUCTION motors, *NONLINEAR theories, *GENETIC algorithms
Abstract

Predictive torque control (PTC) is a high-performance control method of induction motors (IMs), which is still open to research. It provides many advantages over mature control techniques, such as straightforward implementation, the ability to handle nonlinearities, easy inclusion of additional control objectives, and modulatorfree structure. However, it has problems with the selection of weighting factors (WFs) involved in the cost function in PTC. In conventional PTC, these WFs are generally selected by the trial-and-error method. Also, a few studies optimize these WFs with a multi-objective optimization algorithm using both torque and flux errors. In this paper, the WF associated with the flux component is optimized by a genetic algorithm over the speed errors only. The optimized PTC is verified by simulation studies considering different operating conditions. Finally, good control performance has been achieved. [ABSTRACT FROM AUTHOR]

Published
2022
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24. Hız-Algılayıcılı Doğrudan Vektör Kontrollü Asenkron Motor Sürücüleri için İndirgenmiş Dereceli Genişletilmiş Kalman Fitresi Tabanlı Stator ve Rotor Direnci Kestiricisi

Author

DEMİR, Rıdvan, BARUT, Murat, YILDIZ, Recep, ZERDALİ, Emrah, and İNAN, Remzi

Subjects
Asenkron motor,doğrudan vektör kontrol,parametre kestirimi,genişletilmiş Kalman filtresi, Induction motor,Direct vector control,parameter estimation,extended Kalman filter
Abstract

Bu çalışmada, hız-algılayıcılı doğrudan vektör kontrollüasenkron motor (ASM) sürücüleri için indirgenmiş dereceli yeni birgenişletilmiş Kalman filtresi (İDGKF) tabanlı kestirici tasarlanarak benzetimtemelli olarak gerçeklenmektedir. Önerilen İDGKF ile DVK tabanlı ASM sürücüsüiçin gerekli olan stator duran eksen takımındaki rotor akılarının αβ- bileşenlerine(ϕα ve ϕβ'ya) ilave olarak değerisıcaklık ve frekansla değişen stator direnci (Rs) ve rotor direnci (Rr) anma hızı, orta hız vedüşük/sıfır hız bölgelerinde eş zamanlı olarak kestirilmektedir. Hız, yükmomenti (tL), Rr ve Rs'nin zorlayıcı değişimlerialtında üretilen senaryolar ile test edilen İDGKF'ye ilişkin benzetim temellikestirim sonuçları ve bu yüzden DVK'lı ASM sürücüsünün kontrol başarımı oldukçatatmin edicidir. Ayrıca İDGKF algoritmasının tam dereceli eşdeğerine göre dahaaz hesap yükü gerektirdiği bu çalışma kapsamında onaylamaktadır., In this study, speed-sensored direct vector control of induction motors (IMs) is implemented by proposing and using a novel reduced order extended Kalman filter (ROEKF). In addition to αβ-components of rotor fluxes in stator stationary axis (ϕα and ϕβ), stator resistance (Rs) and rotor resistance (Rr) whose values vary according to temperature and frequency are simultaneously estimated by the proposed ROEKF in a wide speed range. Under challenging variations of rotor angular velocity, load torque (tL), Rr, and Rs the obtained simulation based estimation results associated with the ROEKF and thus control performances of the designed direct vector controlled IM drive are quite satisfactory and validate to require less computational load then its full-order counter part.

Published
2018

31. Improved speed and load torque estimations with adaptive fading extended Kalman filter.

Author

Zerdali, Emrah, Yildiz, Recep, Inan, Remzi, Demir, Ridvan, and Barut, Murat

Subjects
*STOCHASTIC systems, *PARAMETER estimation, *INDUCTION motors, *KALMAN filtering, *TORQUE, *SPEED, *SCHUR functions
Abstract

Background: Extended Kalman filter (EKF) is one of the most preferred observers for state and parameter estimation of induction motor. To achieve optimal estimations, EKFs require a stochastic system with complete dynamic or measurement equation. However, those equations are partially known in practice and may vary depending on operating conditions, leading to a degradation in the estimation performance of conventional EKFs (CEKFs). Aim: To overcome this drawback, this paper proposes an adaptive fading EKF (AFEKF) observer that can compensate for the effect of the incomplete dynamic equation for the estimations of stator currents, rotor fluxes, rotor mechanical speed, and load torque. Materials & Methods: To show the superiority of AFEKF, its estimation performance is compared to that of CEKF in both simulations and real‐time experiments. Both observers have been implemented through the S‐Function block in Matlab/Simulink so that the same observer blocks can be used in both simulation and experimental studies. For real‐time implementations, a DS1104 controller board is used. In addition, the computational burdens of both CEKF and AFEKF are compared with real‐time experiments. Results and Discussion: The simulation and experimental studies indicate that the forgetting factor in AFKEF clearly improves the estimation performance of CEKF, especially in transient states. It also prevents the observer from diverging. Considering its advantages, the additional computational load that causes an increase in the computational load of about 4% can be ignored. Conclusion: The proposed AFEKF observer significantly improves the estimation performance and compensates for the effect of dynamic model inaccuracies. Its superiority has been validated by simulation and experimental studies. Finally, an observer with a better estimation performance has been proposed with a slight increase in computational load. [ABSTRACT FROM AUTHOR]

Published
2021
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32. HIZ-ALGILAYICISIZ ASENKRON MOTOR KONTROLÜ İÇİN DAĞILIMLI KALMAN FİLTRESİ İLE GERÇEK-ZAMANLI YÜK MOMENTİ VE ROTOR DİRENCİ KESTİRİMİ

Author

BARUT, Murat, YILDIZ, Recep, and ZERDALİ, Emrah

Subjects
Engineering, Electrical and Electronic, Asenkron motor,dağılımlı kalman filtresi,durum ve parametre kestirimi,hız-algılayıcısız kontrol, Induction motor,unscented Kalman filter,state and parameter estimation, Mühendislik, Elektrik ve Elektronik
Abstract

In this study, the real-time application ofunscented Kalman filter (UKF) based estimator simultaneously estimating thestator stationary axis components of the stator currents and the rotor fluxes, therotor mechanical speed, the load torque, and the rotor resistance by usingmeasured stator currents and voltages for speed-sensorless control of inductionmotors (IMs) is firstly introduced to the literature. Thus, the performance ofproposed UKF based estimator is tested by considering measurement errors ofstator voltages/currents in real-time for a wide range of speeds includingzero-speed transitions and speed reversal under step-type and nonlinearuncertainties/variations of the load torque and rotor resistance. According tothe current literature, the results obtained from the UKF algorithm, whichestimates the maximum states and parameters in case of speed-sensorless, showthat estimated states and parameters quickly converge to real values and steady-stateestimation errors are quite satisfactory. With these aspects, it is the firstknown work in the literature., Bu çalışmada, asenkron motorların(ASM’lerin) hız-algılayıcısız kontrolü için ölçülen stator akımları vegerilimleri kullanılarak; stator akımının ve rotor akısının duran eksen takımıbileşenlerini, rotor hızını, yük momentini ve rotor direncini eş zamanlı olarakkestiren dağılımlı Kalman filtresi (DKF) tabanlı kestiricinin gerçek-zamanlıuygulaması literatüre ilk kez tanıtılmaktadır. Böylece tasarlanan DKF tabanlıkestiricinin kestirim başarımı, stator gerilimleri/akımlarının ölçme hatalarınıdikkate alarak, yük momenti ve rotor direncinin basamak biçimli ve doğrusalolmayan belirsizlikleri/değişimleri altında sıfır hız geçişlerini ve hızterslendirmelerini de içeren geniş bir hız bölgesi için gerçek-zamanlı olaraktest edilmiş olmaktadır. Mevcut literatüre göre hız-algılayıcısız olarak enfazla durum ve parametre kestirimi yapan DKF algoritmasından elde edilensonuçlar, kestirilen durum ve parametrelerin hızlı bir şekilde gerçekdeğerlerine yakınsadığını ve sürekli haldeki kestirim hatalarının oldukçatatmin edici olduğunu göstermektedir. Bu yönleri ile literatürde bilinen ilkçalışmadır.&nbsp

Published
2017

35. Asenkron motor kontrolü için yapay zeka tabanlı algoritmaların geliştirilmesi ve gerçek-zamanlı uygulamaları

Author

Zerdali, Emrah, Barut, Murat, and Elektrik-Elektronik Mühendisliği Anabilim Dalı

Subjects
Artificial intelligence, Yapay zeka, Elektrik ve Elektronik Mühendisliği, Hız-algılayıcısız kontrol, Asenkron motor, Induction motor, Three phase induction motors, Speed-sensorless control, Electrical and Electronics Engineering
Abstract

Bu tez çalışmasında, asenkron motorların (ASM'lerin) hız-algılayıcısız kontrolü için yapay zeka tabanlı kestirim algoritmalarının geliştirilmesi ve gerçek-zamanlı uygulamaları gerçekleştirilmiştir. Bu amaçla, literatürdeki ASM'lerin hız-algılayıcısız kontrolünde kullanılan çalışmalar incelenerek, tez çalışması kapsamında dört farklı çalışma önerilmiştir. İlk çalışmada, hız-algılayıcısız ASM kontrolünde kullanılan modele uyarlamalı sistemlerin uyarlama mekanizmasındaki geleneksel PI kontrolör yerine, kestirim başarımını arttırmak amacıyla iki adet optimize edilmiş bulanık mantık kontrolör (BMK) tasarımı gerçekleştirilmiştir. İkinci çalışmada, çift-girişli genişletilmiş Kalman filtresi (GKF) algoritmasının tasarımı gerçekleştirilmiş ve yüksek başarımlı kestirimler elde edebilmek amacıyla çift-girişli GKF'ye ait sistem (Q) ve ölçme (R) gürültü kovaryans matrisleri, zaman alıcı deneme-yanılma yöntemi yerine, genetik algoritma yardımıyla belirlenmiştir. Üçüncü çalışmada, sezgisel algoritmaların ve maliyet fonksiyonlarının GKF optimizasyonu üzerindeki etkisi incelenmiştir. Gerçekleştirilen dördüncü çalışmada, ASM'nin çalışma koşullarından etkilenen ama genellikle sabit olarak kabul edilen GKF'ye ait Q ve R matrisleri tasarlanan BMK'lar yardımıyla ASM'nin çalışma koşullarına göre çevrimiçi olarak güncellenmiştir., In this thesis, developments and real-time implementations of artificial intelligent based algorithms are performed for speed-sensorless control of induction motors (IMs). For this purpose, studies on speed-sensorless control of IMs are investigated in the literature, and four different studies are proposed in this thesis. In the first study, instead of conventional PI controller which is utilized in the adaptation mechanism of model reference adaptive systems used for speed-sensorless control of IMs, two optimized fuzzy logic controllers (FLCs) are designed in order to increase the estimation performance. Secondly, bi-input extended Kalman filter (EKF) algorithm is designed, and in order to obtain high performance estimations its system (Q) and measurement (R) noise covariance matrices are optimized by using genetic algorithm instead of time-consuming trial-and-error method. Thirdly, the effect of heuristic algorithms and fitness functions on EKF optimization is investigated. In the last fourth study, the elements of ... and ...noise covariance matrices in EKF algorithm, which are affected by operation conditions of IM but generally kept as constant, are online updated by the designed FLCs.

Published
2016

41. Hız-algılayıcısız genişletilmiş kalman filtresi tabanlı kestirici tasarımının genetik algoritma ile optimizasyonu

Author

Zerdali, Emrah, Barut, Murat, and Elektrik-Elektronik Mühendisliği Ana Bilim Dalı

Subjects
Optimization, Induction motors, Elektrik ve Elektronik Mühendisliği, Genetic algorithms, Electrical and Electronics Engineering
Abstract

Bu tez çalışmasında, Asenkron motorların (ASM'lerin) hız-algılayıcısız kontrolü için ölçülen stator faz gerilimleri ve akımlarını kullanarak, stator akım bileşenleri, rotor akısı bileşenleri, hız ve yük momentini kestiren genişletilmiş Kalman filtresindeki (GKF'deki) modelleme hatalarını temsil eden sistem kovaryans matrisi ve ölçme hatalarını temsil eden ölçme kovaryans matrisi elemanları, gerçel-değerli genetik algoritma (GDGA) ile optimize edilmiştir.Bilindiği gibi, herhangi bir GKF algoritmasının başarımı, sistem ve ölçme gürültüsü kovaryans matrisi elemanlarının doğru seçilmesine bağlıdır. Birçok gerçek-zamanlı uygulamada, kovaryans matrisleri deneme-yanılma yöntemi ile bulunmaktadır. Bu yaklaşım hem zaman alıcı bir süreç içermekte hem de matris elemanları deneme-yanılma yöntemiyle bulunduğu için kullanışlı olmamaktadır. ASM'lerin durum kestirimine yönelik literatürdeki mevcut optimize edilmiş GKF tabanlı çalışmalardan farklı olarak bu tez çalışmasında daha yüksek dereceden (6. dereceden) genişletilmiş ASM modelini kullanan GKF algoritması optimize edilmiştir. Böylece, literatürdeki mevcut optimize edilmiş GKF tabanlı çalışmalardan daha fazla sayıda optimize edilmişdurum ve parametre kestirimi gerçekleştirilmiştir. Önerilen yöntemin etkinliği hem benzetimlerle hem de gerçek-zamanlı uygulamalarla doğrulanmıştır. Bu yönleriyle, literatürde bilinen ilk çalışmadır. In this thesis, for the speed-sensorless control of induction motors (IMs), the elements of the system and the measurement covariance matrices representing modeling errors and measurement errors, respectively, in the extended Kalman filter (EKF) algorithm, which includes the simultaneous estimations of stator currents, rotor fluxes, rotor angular velocity, and load torque by using the measured stator currents and voltages, are optimized by real-valued genetic algorithm (GA).As it is well-known, the performance of any EKF algorithm depends on correct choice of the covariance matrix elements of the system and measurement noise. In many real-time applications, the elements of the covariance matrices are found by the trial and error method. This method includes time-consuming process, and also it is not useful since the elements of the covariance matrices are determined by a trial and error method. Differently from the optimized EKF based studies existing in the literature associated with IM state estimations, the EKF algorithm using 6th order extended IM model is optimized in this thesis; thus, comparing with the optimized EKF based studies in the literature, more optimized state and parameter estimations are achieved. The effectiveness of the proposed method in this thesis is verified by simulations and real-time experiments. From these points of view, it is first known study in the literature. 76

Published
2011

45. Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Author

Emrah Zerdali, Murat Barut, 0-Belirlenecek, Zerdali, Emrah -- 0000-0003-1755-0327, and [Zerdali, Emrah -- Barut, Murat] Nigde Univ, Dept Elect & Elect Engn, Nigde, Turkey

Subjects
General Computer Science, Stator, Computer science, 020209 energy, media_common.quotation_subject, Mühendislik, extended Kalman filter, 02 engineering and technology, Inertia, Wound rotor motor, law.invention, Extended Kalman filter, Engineering, law, Control theory, inertia estimation, 0202 electrical engineering, electronic engineering, information engineering, sensorless control, Electrical and Electronic Engineering, Induction motor, media_common, Rotor (electric), Squirrel-cage rotor, 020208 electrical & electronic engineering, Induction motor,extended Kalman filter,sensorless control,rotor-stator resistance estimation,load torque estimation,inertia estimation, Direct torque control, load torque estimation, rotor-stator resistance estimation
Abstract

WOS: 000378097800089, This study aims to develop a novel version of bi input-extended Kalman filter (BI-EKF)-based estimation technique in order to increase the number of state and parameter estimations required for speed-sensorless direct vector control (DVC) systems, which perform velocity and position controls of induction motors (IMs). For this purpose, all states required for the speed-sensorless DVC systems, besides the stator resistance R-s, the rotor resistance R-r, the load torque t(L) including the viscous friction term, and the reciprocal of total inertia 1/j(T), are simultaneously estimated by the novel BI-EKF algorithm using the measured phase currents and voltages. The effectiveness of the proposed speed-sensorless DVC systems is tested by simulations under the challenging variations of R-s, R-r, t(L), j(T), and velocity/position reference. Later, the state and parameter estimations of the novel BI-EKF algorithm are confirmed with real-time experiments in a wide speed range. Finally, in both transient and steady states, a satisfactory estimation and control performance that make this study unique are achieved., Scientific and Technological Research Council of Turkey (TUBITAK) [EEEAG-108E187], This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under research grant EEEAG-108E187.

Published
2016

46. Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors

Author

Emrah Zerdali, Ridvan Demir, Remzi Inan, Murat Barut, Zerdali, Emrah -- 0000-0003-1755-0327, [Barut, Murat -- Zerdali, Emrah -- Inan, Remzi] Nigde Univ, Dept Elect & Elect Engn, TR-51245 Nigde, Turkey -- [Demir, Ridvan] Nigde Univ, Elect & Energy Dept, Bor Vocat Sch, TR-51700 Nigde, Turkey, and 0-Belirlenecek

Subjects
Engineering, induction motors (IMs), business.industry, Rotor (electric), Stator, Estimator, Kalman filter, law.invention, Extended Kalman filter, Control and Systems Engineering, law, Control theory, sensorless control, Torque, Algorithm design, rotor and stator resistance estimation, Electrical and Electronic Engineering, business, load torque estimation, Induction motor
Abstract

WOS: 000305748500021, This paper presents the real-time implementation of a bi input-extended Kalman filter (EKF) (BI-EKF)-based estimator in order to overcome the simultaneous estimation problem of the variations in stator resistance R-s and rotor resistance R-r' aside from the load torque t(L) and all states required for the speed-sensorless control of induction motors (IMs) in the wide speed range. BI-EKF algorithm consists of a single EKF algorithm using consecutively two inputs based on two extended IM models developed for the simultaneous estimation of R-r' and R-s. Therefore, from the point of real-time implementation, it requires less memory than previous EKF-based studies exploiting two separate EKF algorithms for the same aim. By using the measured stator phase voltages and currents, the developed estimation algorithm is tested with real-time experiments under challenging variations of R-s, R-r', and t(L) in a wide speed range; the results obtained from BI-EKF reveal significant improvement in the all estimated states and parameters when compared with those of the single EKFs estimating only R-r' or R-s., Scientific and Technical Research Council of Turkey (Turkiye Bilimsel ve Teknolojik Arastirma Kurumu-TUBITAK) [EEEAG-108E187], This work was supported by the Scientific and Technical Research Council of Turkey (Turkiye Bilimsel ve Teknolojik Arastirma Kurumu-TUBITAK) under the research grant of EEEAG-108E187.

Published
2012

47. Design of hybrid adaptive extended kalman filter for state and parameter estimation of induction motor

Author

Özkurt, Gizem and Zerdali, Emrah

Subjects
Adaptive Kalman filter, State and parameter estimation, Durum ve parametre kestirimi, Uyarlamalı Kalman filtresi, Asenkron motor, Induction motor
Abstract

Asenkron motorlar (ASM'ler) endüstride en yaygın kullanılan tahrik makinalarıdır. ASM'lerin durum/parametre kestirimi bu makinelerin yüksek başarımlı kontrolü ve hız algılayıcısız kontrolü için önem arz etmektedir. Genişletilmiş Kalman filtresi (GKF) sistem ve ölçme gürültülerini de hesaba katarak durum/parametre kestirimine olasıl bir yaklaşım sunmaktadır. Bununla birlikte, GKF ile en iyi kestirimlerin gerçekleştirebilmesi için ASM'ye ait hem dinamik hem de istatiksel model parametrelerinin bilgilerine eksiksiz ihtiyaç duyulmaktadır. Bu bilgilerin çoğu uygulamada bilinmemesi veya kısmen bilinmesi geleneksel GKF'lerin başarımını düşürmektedir. ASM'nin durum/parametre kestiriminde, eksik sistem ve ölçüm gürültü kovaryansları filtreleme başarımı üzerinde en büyük etkiye sahiptir. Bu sorunun üstesinden gelmek için uyarlamalı GKF yöntemleri önerilmiştir. Bununla birlikte, mevcut uyarlama yapıları ile ilgili literatürde belirtilen sıkıntılar bu yöntemlerin kullanımını sınırlandırmaktadır. Bu tez çalışmasında, literatürde mevcut olan uyarlama yöntemlerinin üstünlüklerini bir araya getiren karma bir uyarlama yapısının tasarımı gerçekleştirilmiştir. Önerilen karma uyarlamalı GKF gözlemleyicisi ASM'nin durum/parametre kestirim sorununa uygulanarak başarımı test edilmiştir. Önerilen yöntem hem benzetim hem de deneysel çalışmalarla doğrulanmıştır., Induction motors (IMs) are the most widely used drive machines in the industry. State/parameter estimation of IMs is crucial for high-performance control and speed sensorless control of these machines. Extended Kalman filter (EKF) provides a stochastic approach to state/parameter estimation, taking into account system and measurement noises. However, in order to make optimal estimations with GKF, the complete information of both dynamic and statistical model parameters of IM is required. Not knowing or partially knowing this information in most applications reduces the performance of conventional EKFs. In the state/parameter estimation of IM, incomplete system and measurement noise covariances have the greatest effect on the filtering performance. Adaptive EKF methods have been proposed to overcome this problem. However, the problems stated in the literature regarding existing adaptation mechanisms limit the use of these methods. In this thesis, a hybrid adaptation mechanism that combines the advantages of current adaptation methods has been designed. The performance of the proposed hybrid adaptive EKF observer has been tested by applying the state/parameter estimation problem of IM. The proposed method has been verified by both simulation and experimental studies. Keywords: Induction motor, adaptive Kalman filter, state and parameter estimation

Published
2021

48. Speed-Sensorless Induction Motor Drive with Unscented Kalman Filter Including the Estimations of Load Torque and Rotor Resistance

Author

Murat Barut, Emrah Zerdali, Recep Yildiz, 0-Belirlenecek, and [Yildiz, Recep -- Barut, Murat -- Zerdali, Emrah] Nigde Univ, Dept Elect & Elect Engn, Nigde, Turkey

Subjects
0209 industrial biotechnology, Engineering, Vector control, Rotor (electric), Squirrel-cage rotor, Stator, business.industry, 020208 electrical & electronic engineering, 02 engineering and technology, Kalman filter, Wound rotor motor, 0-Belirlenecek, law.invention, Quantitative Biology::Subcellular Processes, 020901 industrial engineering & automation, law, Control theory, Range (aeronautics), 0202 electrical engineering, electronic engineering, information engineering, business, Induction motor
Abstract

42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON) -- OCT 24-27, 2016 -- Florence, ITALY, WOS: 000399031203037, In this paper, an unscented Kalman filter (UKF) based speed-sensorless vector control of induction motors (IMs) have been implemented for a wide speed range including zero speed. The proposed UKF simultaneously estimates stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance. The effectiveness of the introduced UKF algorithm and thus the speed-sensorless IM drive are verified by computer simulations consisting of different challenging scenarios. From this point of view, it is the first speed-sensorless IM drive in the literature to utilize the UKF algorithm including the simultaneous estimations of stator currents, rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance in simulation., IEEE Ind Elect Soc, Inst Elect & Elect Engineers, Nigde University Research Project Unit, This work is supported by Nigde University Research Project Unit under the research grant of FEB 2016/09-BAGEP.

Published
2016

49. The Optimization of EKF Algorithm based on Current Errors for Speed-Sensorless Control of Induction Motors

Author

Emrah Zerdali, Murat Barut, Ertan, HB, [Zerdali, Emrah -- Barut, Murat] Nigde Univ, Dept Elect & Elect Engn, Nigde, Turkey, and 0-Belirlenecek

Subjects
Engineering, business.industry, Stator, Rotor (electric), Process (computing), Angular velocity, 0-Belirlenecek, law.invention, Extended Kalman filter, Control theory, law, Torque, Current (fluid), business, Induction motor
Abstract

Int Aegean Conference on Electrical Machines and Power Electronics (ACEMP) / Int Conference on Optimization of Electrical and Electronic Equipment (OPTIM) / Int Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION) -- SEP 02-04, 2015 -- Side, TURKEY, WOS: 000382957000064, In this study, an optimized extended Kalman filter (EKF) is designed in order to achieve high performance speed-sensorless control of induction motors (IMs). The EKF algorithm estimates the stationary axis components of rotor fluxes, phi(r alpha) and phi(r beta), the rotor angular velocity, omega(m), the rotor resistance, R'(r), besides the stationary axis components of stator currents, i(s alpha) and i(s beta), which are measured as output as well. Also, an optimization process is performed using errors between measured and estimated current components to obtain satisfying estimation performance with EKF. The experimental results prove the high performance control of the proposed system., IEEE, IEEE Ind Elect Soc, IEEE Power Elect Soc, IEEE Power & Energy Soc, Middle E Tech Univ, Transilvania Univ Brasov, Univ Politechnica Timisoara, Tech Univ Cluj Napoca, IEEE Ind Applicat Soc

Published
2015

50. MRAS BASED REAL-TIME SPEED-SENSORLESS CONTROL OF INDUCTION MOTOR WITH OPTIMIZED FUZZY-PI CONTROLLER

Author

Murat Barut, Emrah Zerdali, [Zerdali, Emrah -- Barut, Murat] Nigde Univ, Elect & Elect Dept, Nigde, Turkey, and 0-Belirlenecek

Subjects
Engineering, Rotor (electric), business.industry, Open-loop controller, PID controller, Control engineering, Fuzzy control system, fuzzy logic controller, law.invention, law, Control theory, Adaptive system, induction machine, sensorless control, model reference adaptive system, business, MRAS, Induction motor, differential evolution algorithm
Abstract

IEEE International Symposium on Sensorless Control for Electrical Drives and Predictive Control of Electrical Drives and Power Electronics (SLED/PRECEDE) -- OCT 17-19, 2013 -- Munich, GERMANY, WOS: 000330766800026, In this paper, rotor flux-oriented model reference adaptive system (RF-MRAS) based estimators are designed to obtain flux and speed estimations for speed-sensorless control of induction motors (IMs). The proposed RF-MRAS in this work replaces Conventional PI controller (CPI) in adaptation mechanism of RF-MRAS with fuzzy-PI (FPI) controller in order to improve conventional RF-MRAS. Additionally, the gains of both FPI and CPI controllers are optimized by offline via differential evolution algorithm (DEA) to make fair comparisons and without using time-consuming process of trial-and-error method., IEEE, IEEE Power Elect Soc, Tech Univ Munchen

Published
2013

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