Differential Evolution Applied to DTC Drive for Three-Phase Induction Motors Using an Adaptive State Observer

Costa, Bruno Leandro Galvão; Angélico, Bruno Augusto; Goedtel, Alessandro; Castoldi, Marcelo Favoretto; Graciola, Clayton Luiz

doi:10.1007/s40313-015-0188-0

Cited by 15 publications

(5 citation statements)

References 33 publications

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“…Metaheuristics are an iterative processes for solving different optimization problems by taking into account simple trial operations [15,16]. Particle-swarm optimization [17], genetic algorithm [18], Imperialist competitive algorithm [19], ant colony optimization [20,21], differential evolution [22,23] are commonly treated. In [24], water cycle algorithm was adopted to adjust the parameters of PI controller for speed control and reactive power control loops in RSC (rotor side converter) and DC-link regulation voltage loop in GSC (grid side converter).…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System

et al. 2020

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In this study, a simulation-based coyote optimization algorithm (COA) to identify the gains of PI to ameliorate the water-pumping system performance fed from the photovoltaic system is presented. The aim is to develop a stand-alone water-pumping system powered by solar energy, i.e., without the need of electric power from the utility grid. The voltage of the DC bus was adopted as a good candidate to guarantee the extraction of the maximum power under partial shading conditions. In such a system, two proportional-integral (PI) controllers, at least, are necessary. The adjustment of (Proportional-Integral) controllers are always carried out by classical and tiresome trials and errors techniques which becomes a hard task and time-consuming. In order to overcome this problem, an optimization problem was reformulated and modeled under functional time-domain constraints, aiming at tuning these decision variables. For achieving the desired operational characteristics of the PV water-pumping system for both rotor speed and DC-link voltage, simultaneously, the proposed COA algorithm is adopted. It is carried out through resolving a multiobjective optimization problem employing the weighted-sum technique. Inspired on the Canis latrans species, the COA algorithm is successfully investigated to resolve such a problem by taking into account some constraints in terms of time-domain performance as well as producing the maximum power from the photovoltaic generation system. To assess the efficiency of the suggested COA method, the classical Ziegler–Nichols and trial–error tuning methods for the DC-link voltage and rotor speed dynamics, were compared. The main outcomes ensured the effectiveness and superiority of the COA algorithm. Compared to the other reported techniques, it is superior in terms of convergence rapidity and solution qualities.

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Section: Introductionmentioning

confidence: 99%

Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System

et al. 2020

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“…ASM'lerin değişen bu parametreler ile birlikte kontrolü için gerekli akı gibi durumların kestirimini gerçekleştirmek üzere literatürde yeni yöntemlerinin geliştirilmesi çalışmaları devam etmektedir. ASM sürücülerinin performansı, kestirim yöntemlerinin başarımından doğrudan etkilendiği için modele uyarlamalı sistem (MUS) [3], yapay sinir ağları (YSA) [4], genişletilmiş Luenberger gözlemleyicisi [5] ve genişletilmiş Kalman filtresi (GKF) [6][7][8] [19] ise çevrimiçi mıknatıslama endüktansı kestiricisi ve TD-GKF algoritmasının eş-zamanlı çalıştırılması ile oluşturulan "karma kestirim algoritması" ile , ve 'nin eş zamanlı kestirimi gerçekleştirilmiştir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Asenkron Motor Sürücüleri için İndirgenmiş Dereceli Genişletilmiş Kalman Filtresi Tabanlı Parametre Kestirimi

Demir

Barut

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, a speed-sensored reduced-order extended Kalman filter (RO-EKF) based estimator is designed for direct vector control system of induction motors (IMs). The rotor resistance, stator resistance, and magnetizing inductance are simultaneously estimated by the designed estimator. The proposed estimator is tested by simulations for a wide speed range. The proposed estimation algorithm is known as the first RO-EKF algorithm in the literature which estimates the stator resistance, rotor resistance, and magnetizing inductance simultaneously. Figure A. Block schema of the direct vector controlled induction motor drive Purpose: The aim of this study is to introduce a direct vector control based high-performance IM driver which uses RO-EKF algorithm for state and parameter estimation. Theory and Methods: IMs have parameters which vary the operating conditions such as temperature, frequency, and voltage level. Thus variations of these parameters make control performances of IM difficult. To overcome these disadvantages, an RO-EKF estimating the stator resistance, rotor resistance, magnetizing inductance, and rotor flux in a wide speed range including very low/zero-speed and field-weakening zone is designed. Results: Simulation results show that the proposed estimation algorithm and thus the speedsensored direct vector control based induction motor drive utilizing the designed estimation algorithm are quite satisfactory. Conclusion: In this study, a new state and parameter estimator is presented for the IM drives using the speed-sensored RO-EKF algorithm. The proposed estimation algorithm is tested with simulations in a wide speed range with step and ramp changes of rotor resistance, stator resistance, magnetization inductance, and load torque. The simulation results verify that the proposed estimation algorithm and the speed-sensored DVC system using the proposed estimation algorithm are quite satisfactory.

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“…Deterministic model-based methods, such as adaptive flux (Salmasi et al, 2010), extended Luenberger (Costa et al, 2015), or sliding-mode observers (Kassem, 2016), model reference adaptive systems (Kumar et al, 2015), artificial neural networks (Wlas et al, 2008), and the other nonlinear design techniques (Marino et al, 2005). Most studies using deterministic models of induction motors ignore R s (or R r ) estimation and persistent-zero-speed operation and need an additional high-frequency exciting signal, which results in fluctuations in the developed torque and rotor speed, as in Marino et al (2005), who also do not consider the drawbacks of a stator voltage model providing open-loop flux estimation, which is very sensitive to noise and R s variations at very low speeds, in simulation.…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control

Demir

Barut

2017

Transactions of the Institute of Measurement and Control

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This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance ([Formula: see text]) and rotor resistance ([Formula: see text]) for speed-sensorless induction motor control. The EKF simultaneously estimates the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator currents, the stator stationary axis components ([Formula: see text] and [Formula: see text]) of stator fluxes, rotor angular velocity ([Formula: see text]), load torque ([Formula: see text]) and [Formula: see text], while the AP-MRAS provides the online [Formula: see text] estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a [Formula: see text] reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.

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Differential Evolution Applied to DTC Drive for Three-Phase Induction Motors Using an Adaptive State Observer

Cited by 15 publications

References 33 publications

Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System

Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System

Asenkron Motor Sürücüleri için İndirgenmiş Dereceli Genişletilmiş Kalman Filtresi Tabanlı Parametre Kestirimi

Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control

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