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Induction motors (IMs) are widely used in industrial applications due to their advantages over other motor types. However, the efficiency and lifespan of IMs can be significantly impacted by operating conditions, especially Unbalanced Supply Voltages (USV), which are common in industrial plants. Detecting and accurately assessing the severity of USV in real-time is crucial to prevent major breakdowns and enhance reliability and safety in industrial facilities. This paper presented a reliable method for precise online detection of USV by monitoring a relevant indicator, denominated by negative voltage factor (NVF), which, in turn, is obtained using the voltage symmetrical components. On the other hand, impedance estimation proves to be fundamental to understand the behavior of motors and identify possible problems. IM impedance affects its performance, namely torque, power factor and efficiency. Furthermore, as the presence of faults or abnormalities is manifested by the modification of the IM impedance, its estimation is particularly useful in this context. This paper proposed two machine learning (ML) models, the first one estimated the IM stator phase impedance, and the second one detected USV conditions. Therefore, the first ML model was capable of estimating the IM phases impedances using just the phase currents with no need for extra sensors, as the currents were used to control the IM. The second ML model required both phase currents and voltages to estimate NVF. The proposed approach used a combination of a Regressor Decision Tree (DTR) model with the Short Time Least Squares Prony (STLSP) technique. The STLSP algorithm was used to create the datasets that will be used in the training and testing phase of the DTR model, being crucial in the creation of both features and targets. After the training phase, the STLSP technique was again used on completely new data to obtain the DTR model inputs, from which the ML models can estimate desired physical quantities (phases impedance or NVF). [ABSTRACT FROM AUTHOR]

Autonomous electric vehicles (AEVs) have garnered increasing attention in recent years as they hold significant promise for transforming the transportation sector. However, despite advances in the field, effective vehicle drive control remains a critical challenge that must be addressed to realize the full potential of AEVs. This study presents a novel approach to AEV drive control for concurrently generating a suitable speed profile and controlling the vehicle drive speed along a planned path that takes into account various driving circumstances that mimic real-world driving. The designed strategy is divided into two parts: The first part presents a proposed speed planning algorithm (SPA) that works on developing an adequate speed profile for vehicle navigation; first, the algorithm uses an approach for identifying sharp curves on the predefined trajectory; secondly, based on the dynamic properties of these curves, it generates an appropriate speed profile to ensure smooth vehicle travel across the entire trajectory with varying velocities. The second part proposes a new back-stepping control technique with a space vector modulation (SVM) strategy to control the speed of an induction motor (IM) as a traction part of the AEV. A load torque observer has been designed to enhance the speed-tracking task, while the system stability has been proven using Lyapunov theory. Through a series of experiments and simulations using MATLAB/Simulink software and the dSPACE 1104 real-time interface, we demonstrate the effectiveness of the SPA combined with the back-stepping control technique and highlight its potential to advance the field of AEV technology. Our findings have important implications for the design and implementation of AEVs and provide a foundation for future research in this exciting area of study. [ABSTRACT FROM AUTHOR]

Multiphase drives have been presented as potential replacements for conventional three-phase machines, primarily because of their propensity to operate faultlessly. Due to the various stator phase arrangements, standard fault detection techniques are insufficiently applicable and cannot be used to diagnose faults in the various configurations of multiphase machines in closed-loop applications. The current study proposes an effective online diagnostic technique based on the computing and tracking of a significant severity factor, which is defined as the ratio of the zero, negative, and positive voltage symmetrical components employing a short-time least-square Prony algorithm (STLSP). In this study, the asymmetrical six-phase induction motor (ASPIM) was controlled by a model predictive control (MPC) algorithm, an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. This article addresses stator faults in ASPIMs. The effectiveness of the suggested strategy was confirmed experimentally for various operating conditions in both steady and transient states. [ABSTRACT FROM AUTHOR]

The conventional model predictive control (MPC) is an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. However, as the number of phases increases, the MPC's complexity increases exponentially, posing a high computational burden. Additionally, the MPC still presents other issues related to the weighting factor design in the cost function. Accordingly, this paper proposes a low-complexity hysteresis model predictive current control (HMPCC) that can significantly reduce the computational burden, improve the motor's performance, and completely avoid the weighting factor design. The proposed method is a hybrid control method, consisting of two distinct controls that complement one another. The hysteresis control is used to reduce the number of iterations per sampling period, thereby reducing the computational effort required to choose the voltage vector that actively produces torque/flux, and nullifying the weighting factor requirement. Finally, the MPC is used to improve the torque and current quality. The effectiveness of the proposed method is verified through experimental data, and the results emphasize the improvement of the proposed HMPCC scheme. [ABSTRACT FROM AUTHOR]

Multiphase machines have recently been promoted as a viable alternative to traditional three-phase machines. Most experts are looking for strategies to estimate the rotation speed of such complex systems, since speed data are required for high-performance control purposes. Traditionally, electromechanical sensors were used to detect the rotor speed of electric motors. These devices are extremely accurate, but they are also delicate and costly to deploy. New speed estimating algorithms must be created for these situations. This paper looks at how to estimate rotor speed in symmetrical six-phase induction motors (IMs) using a novel strategy for rotor speed estimation based on the Short Time Fourier Transform (STFT) method. The technique is based on tracking the frequencies of the rotor slot harmonics (RSH) seen in most squirrel-cage IM stator currents, thus assuring a broad range of applications. To monitor the RSH, the STFT employs a sliding window to perform the discrete Fourier transform technique, making it more suitable for online use with noisy and nonstationary signals. Experimental tests demonstrate the effectiveness of the suggested approach. [ABSTRACT FROM AUTHOR]

Three-phase induction motors are widely used in industrial facilities, where the maintenance of these machines is preponderant for industrial processes. Recent research reports that vibration-based data acquisition is the most common approach to perform bearing condition monitoring because it can extract more relevant information. However, the acquisition of vibration-based signals is expensive, requiring accelerometers and other external devices to transmit and process the signal information. Otherwise, current-based signals are directly measured by the supply system or inverters, enabling the current-based data acquisition in most industrial cases. In this context, this work introduces a new current-based method to identify bearing damages, applying artificial intelligence algorithms. Experimental and on-site tests present promising results, validating this approach for bearing damage diagnosis. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to develop a high-level performance, and low-cost current sensorless control strategy for Induction Motor (IM) drives. Therefore, a new phase-current regeneration method, for current sensorless vector control in induction motor drives is introduced. The idea is based on the reconfiguration of the Luenberger adaptive observer for currents estimation, using the information provided by the dc-link voltage sensor. The basis of the proposed control and the theoretical study of the modified adaptive observer are presented. Several simulation and experimental tests were performed on an induction motor of 1.1 kW working under different operating conditions. The obtained results prove and testify the relevance, workability, and practicability of the suggested currents sensorless vector control strategy. • A novel strategy for three-phase induction motor currents estimation is proposed. • Improvement of current sensorless vector controlled induction motor. • Hardware implementation testifies the workability of the proposed estimator. • Various tests highlight the current sensorless control good-level performance. • Control system costs reduction has been achieved for industrial applications. [ABSTRACT FROM AUTHOR]

In this article, the occurrence of stator faults in line-start permanent magnet synchronous motors is analyzed. A dynamic model of the motor was developed in MATLAB/Simulink, which considers the performance of the motor both in healthy conditions and under the occurrence of interturn short-circuit faults, at an early stage of development. To validate the presented approach, simulation results, covering the full range of operation, and different fault severity levels, are compared with the motor experimental performance. The output of the proposed model, namely voltages and currents, present a good agreement with experimental results, demonstrating the accuracy of the proposed model. Additionally, an online fault diagnostic technique is explored, demonstrating its potentiality. [ABSTRACT FROM AUTHOR]

Significant changes may occur in the thermal behavior of a directly grid-fed induction machine when subjected to unbalances in the voltage supply. This article studies and analyzes the thermal behavior of a low power, three-phase, squirrel-cage induction motor, connected in star configuration with floating neutral point, when subjected to different levels of unbalanced voltage. The dependence of the thermal motor behavior on the severity level of the unbalance is studied and analyzed. In addition to amplitude unbalances, this article focuses on the effects of phase unbalances, as well, which were not addressed in detail in previous published studies. Moreover, situations of mixed unbalance, where amplitude and phase unbalances occur simultaneously, are also studied. The finite element method was used to simulate the thermal behavior of the machine. The experimental setup consists of a three-phase programmable AC power supply, suitable to precisely emulate unbalanced conditions that may occur in real-scale power systems, supplying a 2.2 kW induction motor. Experimental data were acquired resorting to resistance temperature detectors PT100, placed in the machine phase whose supply current value changed the most. Finally, the simulation results are verified and critically discussed through experimentally obtained results. [ABSTRACT FROM AUTHOR]

Induction motors airgap-eccentricity fault detection has attracted the interest of many researchers in the last decades. The discrete Fourier transform (DFT) has been widely used for that purpose. However, DFT can only give meaningful information for stationary signals. In addition, DFT requires a long acquisition time in order to get a high-frequency resolution. In this paper, an efficient time-domain technique based on a modified Prony's method for the airgap-eccentricity fault detection in induction motors is presented in order to overcome the DFT problems. Using this technique, the stator current and apparent power—as processed signals—are divided into short overlapped time windows and each one is analyzed by the least squares Prony's method. The proposed technique allows tracking the frequency and amplitude of the airgap-eccentricity fault characteristic component with a very high accuracy using only few data samples. A fault severity factor based on the amplitude of the estimated component is defined. The effectiveness and accuracy of the proposed method are verified through the presented simulation and experimental results. [ABSTRACT FROM PUBLISHER]

It is well known that rotor speed estimation assumes a paramount importance for the correct diagnosis of bearings, air-gap eccentricities, or rotor bar defects. In this paper, a new technique for rotor speed estimation using a modified Prony’s method is proposed. The algorithm developed for this purpose is based on tracking the frequencies of the rotor slot harmonics (RSH) that exist in stator currents of most squirrel-cage induction motors. High-order RSH frequencies are used to avoid the possible effect of harmonics stemming from other sources. The proposed modified Prony’s method shows a great ability for tracking RSH frequencies and then the rotation speed. In addition, this technique can deal with noisy and nonstationary signals and it requires only few data samples, which reduce considerably the computational time and data storage requirements. Consequently, the proposed algorithm is suitable for online implementation. The method’s effectiveness is verified by simulation and experimental tests. [ABSTRACT FROM AUTHOR]

It has recently been shown that spectrum analysis of instantaneous power can provide sensitive online detection of rotor faults in induction motors compared with current, torque, speed, or vibration spectrum analysis. However, it was reported that monitoring of the twice slip frequency, 2sfs, components induced by the fundamental component can produce false rotor fault alarms due to asymmetry in the rotor or low frequency load oscillations. In this paper, the rotor fault components induced in the power spectrum by the stator fifth and seventh space harmonics are derived to evaluate their immunity to false alarms. It is shown that the (6 - 8s)fs component can provide reliable detection of rotor faults under cases where existing methods produce false alarms. An experimental study performed on custom built rotor samples shows that the new components are capable of detecting rotor faults immune to false alarms produced by rotor axial air ducts, rotor anisotropy, and low frequency load oscillations for cases where existing methods fail. The components derived in this paper can also be applied to vibration, speed, torque, or acoustic monitoring for reliable detection of rotor faults. [ABSTRACT FROM AUTHOR]

The knowledge of the broken rotor bar characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The discrete Fourier transform (DFT) has been widely used to achieve these requirements. However, DFT can give meaningful information only for stationary harmonics which cannot be guaranteed in real cases. In addition, a long data sequence is necessary for DFT to get high frequency resolution. Nevertheless, the signals are time varying, and the steady-state conditions can be lost for a long time acquisition. As a solution for these problems, this paper proposes an efficient time-domain technique based on a modified Prony method for the estimation of the frequencies/amplitudes of broken rotor bar faults. Using this technique, the stator current is divided into short overlapped time windows, and each one is analyzed by the least squares Prony method. The proposed technique provides a linear time–frequency/amplitude representation with high frequency resolution and adjustable time resolution. It is shown that this technique allows tracking the frequencies and amplitudes of the sidebands around the fundamental frequency component with a very high accuracy. The efficiency of the proposed method is verified by simulation and experimental tests. [ABSTRACT FROM AUTHOR]

In this paper, a new detection technique based on the instantaneous-reactive-power signature analysis is proposed for the diagnosis of rotor-cage faults (RCFs) in operating three-phase induction motors (IMs). This technique has been tested through the simulation of two different IMs using a mathematical model based on the winding-function approach. In order to demonstrate the capability of the proposed tool for rotor-condition monitoring, these simulations are complemented by the experimental results obtained from two IMs with several faulty rotors, powered from sinusoidal and nonsinusoidal supply voltages. The results obtained by the fast-Fourier-transform algorithm and the Welch method show the merits of the proposed approach for the detection of RCFs. A suitable fault-severity factor is also proposed as an indicator of the motor condition. Moreover, the inductive reactive effect of RCFs is underlined to demonstrate the effectiveness and convenience of this new technique for the diagnosis of RCFs in three-phase IMs. [ABSTRACT FROM AUTHOR]

Detection of broken rotor bars has been an important but difficult job in the detection area of induction motor faults. The characteristic frequency component of a faulty rotor in the stator current spectrum is very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection, In this paper a new detection technique based on the α, β instantaneous active power signature analysis is proposed for the diagnosis of rotor cage faults in operating squirrel cage three-phase induction motors (IMs). Experimental results are presented to show the merits of this novel approach. [ABSTRACT FROM AUTHOR]

This paper presents the results of an investigation regarding the thermal behavior of a three-phase induction motor when supplied by a reconfigured three-phase voltage source inverter with fault-tolerant capabilities. For this purpose, a fault-tolerant operating strategy based on the connection of the faulty inverter leg to the dc link middle point was considered. The experimentally obtained results show that, as far as the motor thermal characteristics are concerned, it is not necessary to reinforce the motor insulation properties since it is already prepared for such an operation. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis involving several fault-tolerant operating strategies, applied to three-phase induction-motor drives, that intend to compensate for inverter faults. The results presented show the advantages and the inconveniences of several fault-tolerant drive structures, under different control techniques, such as the field-oriented control and the direct torque control. Experimental results concerning the performance of the three-phase induction motor, based on the analysis of some key parameters, like induction-motor efficiency, motor power factor, and harmonic distortion of both motor line currents and phase voltages, will be presented. [ABSTRACT FROM AUTHOR]

This paper proposes the use of the multiple reference frames theory for the diagnosis of stator faults in three-phase induction motors. The development of a simplified mathematical motor model allowed the establishment of the equivalent circuits of the motor, in d-q-0 axes, in the presence of suitor interturn short circuits. The use of the stationary reference frame, clockwise and counterclockwise synchronous reference frames, allows the extraction and manipulation of the information contained in the motor supply currents in a way that the effects introduced by the fault are easily isolated and measured. A severity factor is defined and the simulation and experimental results presented demonstrate its independence in relation to the working conditions of the motor, such as the load level and unbalances in the voltage supply system. Although the technique is here introduced for the diagnosis of suitor faults, it is possible to extend its use for the diagnosis of other asymmetries such as broken rotor ban and air-gap eccentricity. [ABSTRACT FROM AUTHOR]

This paper deals with the use of a new diagnostic technique based on the multiple reference frames theory for the diagnosis of stator winding faults in a direct-torque-controlled (DTC) induction motor drive. The theoretical aspects underlying the use of this diagnostic technique are presented but a major emphasis is given to the integration of the diagnostic system into the digital-signal-processor (DSP) board containing the control algorithm. Taking advantage of the sensors already built in the drive for control purposes, it is possible to implement this diagnostic system at no additional cost, thus giving a surplus value to the drive itself. Experimental results show the effectiveness of the proposed technique to diagnose stator faults and demonstrate the possibility of its integration in the DSP board. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach, based on the spectral analysis of the motor current Park's Vector modulus, for detecting the occurrence of rotor cage faults in operating three-phase induction machines. The major theoretical principles of the Extended Park's Vector Approach are described. Both simulation and laboratory test results demonstrate how the presence of rotor cage faults can be effectively detected by the application of this new approach. These results are also correlated with the ones obtained by the conventional motor current spectral analysis and by the classic Park's Vector Approach. [ABSTRACT FROM AUTHOR]

Three-phase induction motors (IMs) are the main workhorse in industry due to their many advantages as compared to other types of industrial motors. However, the efficiency and lifetime of IMs can be considerably affected by some operating conditions, in particular those related to unbalanced supply voltages (USV), which is quite a common condition in industrial plants. Therefore, early detection and a precise severity estimation of the USV for all working conditions can prevent major breakdowns and increase reliability and safety of industrial facilities. This paper proposes a reliable method allowing for a precise and online detection of the USV condition, by monitoring a pertinent indicator calculated using the voltage symmetrical components. The effectiveness of the proposed method is validated experimentally for several different working conditions, and a comparison with other indicators available in the literature is also performed. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results. [ABSTRACT FROM AUTHOR]

The requisite of direct-on-line (DOL) starting for various applications in underground mines subjects the rotor bars of heavy-duty squirrel cage induction motors (SCIMs) to severe stresses, resulting in sustained fault in the rotor bars, unlike the applications where mostly reduced voltage starting is preferred. Furthermore, SCIMs working in underground mines are also affected by unforeseen frequency fluctuations. Hence, the paper proposes a discrete wavelet transform (DWT)-based broken rotor bar detection scheme using the stator current analysis of SCIM when subjected to a frequency regulation (±4% of 50 Hz supply) in steady-state, as prevalent in underground mines. In this regard, the level-seven detailed coefficient obtained by the DWT-based multi-resolution analysis of stator current corresponding to the healthy rotor is compared with that of the faulty rotor to extract the necessary features to identify the fault. Further implementation of the proposed scheme is done using artificial neural network (ANN)-based pattern recognition techniques, wherein both feed-forward backdrops and cascaded forward backdrop type ANNs have been used for fault pinpointing based on the feature extraction results obtained from DWT. The scheme is developed and analysed in MATLAB/Simulink using 5.5 kW, 415 V, 50 Hz SCIM, which is further validated using the LabVIEW-based real-time implementation. [ABSTRACT FROM AUTHOR]