Papers by T. Orlowska-Kowalska
IEEE Industrial Electronics Magazine, 2012
The Industrial Electronics Society is an organization, within the framework of the IEEE, of membe... more The Industrial Electronics Society is an organization, within the framework of the IEEE, of members with principal professional interest in electronics and electrical sciences as applied to control, treatment, and measurement of industrial processes. For membership and subscription information and pricing, please visit www.ieee.org/membership-catalog.
Performance analysis of the sensorless adaptive sliding-mode neuro-fuzzy control of the induction... more Performance analysis of the sensorless adaptive sliding-mode neuro-fuzzy control of the induction motor drive with MRAS-type speed estimator
Mathematics and Computers in Simulation, 2015
The paper deals with faults diagnosis and control methods for a faulttolerant direct field-orient... more The paper deals with faults diagnosis and control methods for a faulttolerant direct field-oriented controlled by Space Vector Modulation (SVM) two-level voltage-inverter-fed induction motor drive. In order to maintain an uninterrupted motor drive operation even under its faulty condition, two diagnostic algorithms as well as post-fault control techniques are developed. According to the first diagnostic system, that is related to single open-circuit faults in two-level voltage inverter, a failure diagnosis is performed by monitoring a time presence of a reference inverter voltage vector in particular sectors of the coordinate system. After the fault detection, to maintain a high-quality drive performance, a switch-redundant inverter scheme is utilized. In case of the second diagnostic system, for the speed sensor failure detection, a motor speed estimator, that is based on a model reference adaptive system, is utilized. This algorithm is used for the post-fault motor drive operation as well. Both fault monitoring methods are based on a software solution, therefore they do not generate additional implementation costs. To validate the described fault-tolerant control scheme, chosen simulation tests, which were carried out in MATLAB/Simulink, are presented. Additionally, in case of the transistor failure diagnosis, experimental tests were carried out.
2010 IEEE International Conference on Industrial Technology, 2010
ABSTRACT This paper presents the application of the General Regression Neural Networks in the dia... more ABSTRACT This paper presents the application of the General Regression Neural Networks in the diagnostics of the induction motors. The specific fault symptoms of rotor damages included in measured stator current spectrum are proposed as elements of input vectors of GRNN. The structure and training procedure of such neural detector are described. Diagnostic results obtained by the proposed neural detector of rotor faults are demonstrated.
Energies
Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applica... more Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or...
Electronics
In this article, a low-cost computer system for the monitoring and diagnosis of the condition of ... more In this article, a low-cost computer system for the monitoring and diagnosis of the condition of the induction motor (IM) rolling bearings is demonstrated and tested. The system allows the on-line monitoring of the IM bearings and subsequent fault diagnostics based on analysis of the vibration measurement data. The evaluation of the bearing condition is made by a suitably trained neural network (NN), on the basis of the spectral and envelope analysis of the mechanical vibrations. The system was developed in the LabVIEW environment in such a way that it could be run on any PC. The functionality of the application has been tested on a real object. The study was conducted on a low-power IM equipped with a set of specially prepared bearings so as to model the different damages. In the designed computer system, a selected NN for detecting and identifying the defects of individual components of the induction motor’s bearings was implemented. The training data for NNs were obtained from re...
Energies
Permanent Magnet Synchronous Motor (PMSM) failures are currently widely discussed in the literatu... more Permanent Magnet Synchronous Motor (PMSM) failures are currently widely discussed in the literature, but the impact of these failures on the operation of control systems and the ability to detect selected failures despite the compensating effect of control algorithms being relatively rarely analyzed. The article presents the impact of damage to the stator winding of a PMSM motor on the operation of two frequency control structures, scalar and vector control. The mathematical model of PMSM that takes into account the influence of a different number of shorted turns in the stator winding phase was presented, and its experimental verification was performed. Then, the influence of various degrees of damage to the stator winding on the waveforms of the motor state variables in an open scalar control structure and in a closed field-oriented control structure was analyzed. Based on the analysis of phase currents and rotational speed of the motor as well as the influence of the PMSM motor o...
IEEE Transactions on Industrial Informatics
Robust Control - Theoretical Models and Case Studies, 2016
This chapter deals with sliding mode application in control of an induction motor (IM) torque, sp... more This chapter deals with sliding mode application in control of an induction motor (IM) torque, speed, and position. Classical, direct approaches to control mentioned variables are described. Their drawbacks are presented and analyzed. Direct control structures are then compared with the proposed cascade sliding mode control structures. These structures allow to control all of the IM variables effectively, simultaneously ensuring supervision of all remaining variables. All of the analyzed structures are illustrated with block diagrams, as well as with simulation and experimental test results.
Electronics
This article presents the efficiency of using cascaded neural structures in the process of detect... more This article presents the efficiency of using cascaded neural structures in the process of detecting damage to electrical circuits in a squirrel cage induction motor (IM) supplied from a frequency converter. The authors present the idea of a sequential connection of classic neural structures to increase the efficiency of damage classification and detection presented by individual neural structures, especially in the initial phase of single or multiple electrical failures. The easily measurable axial flux signal is used as a source of diagnostic information. The developed cascaded neural networks are implemented in the measurement and diagnostic software made in the LabVIEW environment. The results of the experimental research on a 1.5 kW IM supplied by an industrial frequency converter confirm the high efficiency of the use of the developed cascaded neural structures in the detection of incipient stator and rotor winding faults, namely inter-turn stator winding short circuits and br...
Energies
In the literature on sensorless control of induction motors, many algorithms have been presented ... more In the literature on sensorless control of induction motors, many algorithms have been presented for rotor flux and speed estimation. However, all these algorithms have been developed in the continuous–time domain. The digital realization of the control systems, requires the implementation of those estimation methods in a discrete–time domain. The main goal of this article is comparison of the impact of different numerical integration methods, used in analogue emulation under the digital implementation of the control systems, to the operation of classical Model Reference Adaptive System; CC-based on two current models (MRASCC) speed estimator and its three modified versions developed for the extension of the estimator stability region. In this paper the generalized mathematical model of MRASCC estimator is proposed, which takes into account all known methods for the extension of the stability region of classical speed estimator of this type. After the short discussion of the discret...
Energies
In this paper, the idea of using a convolutional neural network (CNN) for the detection and class... more In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high acc...
Electronics
Sliding mode control (SMC) of electric drives constitutes a very popular control method for nonli... more Sliding mode control (SMC) of electric drives constitutes a very popular control method for nonlinear multivariable and time-varying systems, e.g., induction motor (IM) drives. Nowadays, IM are the most popular electrical machines (EM) applied in many industrial applications as motion control devices, including electrical and hybrid vehicles. Nowadays, the control systems of EM are mostly realized using digital techniques (microprocessors and microcontrollers). Therefore, all control algorithms should be discretized or the whole control system should be designed in the discrete-time domain. This paper deals with a discrete-time sliding mode control (DSMC) for IM drives. The discrete algorithms for sliding mode control of the motor speed and rotor flux are derived in detail and next tested in simulation research. The simulation tests include the discrete nature of the power converter supplying the IM and present excellent performance of the developed control structure. To obtain the ...
Sensors
Designing electrical drives resistant to the failures of chosen sensors has recently become incre... more Designing electrical drives resistant to the failures of chosen sensors has recently become increasingly popular due to the possibility of their use in fault-tolerant control (FTC) systems including drives for electric vehicles. In this article, a virtual current sensor (VCS) based on an algorithmic method for the reconstruction of the induction motor (IM) phase currents after current sensor faults was proposed. This stator current estimator is based only on the measurements of the DC-bus voltage in the intermediate circuit of the voltage-source inverter (VSI) and a rotor speed. This proposal is dedicated to fault-tolerant vector controlled IM drives, where it is necessary to switch to scalar control as a result of damage to the current sensors. The proposed VCS allows further uninterrupted operation of the direct rotor-field oriented control (DRFOC) of the induction motor drive. The stator current estimator has been presented in the form of equations, enabling its practical impleme...
Energies
This paper presents a comparative study on the application of different neural network structures... more This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.
Power Electronics and Drives
This paper deals with the stability problem of three stator current error-based estimators of ind... more This paper deals with the stability problem of three stator current error-based estimators of induction motor speed, especially in the regenerating operation mode. The stability of the adaptive full-order observer (AFO) and two model reference adaptive systems (MRASs) based on a stator current error (MRASCCand MRASCV) is briefly analysed, and the stability borders are determined and compared. It is shown that MRASCVspeed estimator is stable in the whole operation range including the regenerating mode without any modifications. The stability enhancement method for AFO and MRASCCestimators is described, and the solution for their stability improvement is proposed. Torque-speed characteristics of the analysed MRAStype estimators in a wide range of drive speed and load torque changes are given, as well as the behaviour of estimators during transients is compared. The theoretical analysis and simulation test results are validated by experimental tests.
Power Electronics and Drives
This short article constitutes an introductory part of the Special Section (SS) on State and Para... more This short article constitutes an introductory part of the Special Section (SS) on State and Parameter Estimation Methods in Sensorless Drives. In the current issue of the journal, the first part of this section is published. Accepted articles are focussed mainly on estimation of the state variables and parameters for vector-controlled induction motor (IM) drives, using different concepts, such as different types of Kalman filters (KFs) and model reference adaptive systems (MRASs). The KF was also proposed for brushless DC motor (BLDC). Also, neural networks (NNs) have been proposed for mechanical state variables’ estimation of the drive system with elastic couplings.
IEEE Transactions on Industrial Electronics
Archives of Electrical Engineering
In this paper a transistor open-circuit fault diagnosis method in a rotor field oriented controll... more In this paper a transistor open-circuit fault diagnosis method in a rotor field oriented controlled induction motor drive, fed by a two-level voltage inverter has been proposed. The diagnostic procedure ensures detection and localization of single or multiple power switch failures in time shorter than one period of a stator current fundamental harmonic, without regard to a drive operation point. A new simple scheme of the diagnostic system is proposed. In order to validate the proposed transistor fault diagnostic method, a detailed simulation as well as experimental tests of the field-oriented control drive system were carried out and some of them are shown in this paper.
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Papers by T. Orlowska-Kowalska