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In this paper, an energy-saving scheme in rotor field-orientated vector control is developed for induction motor drives. The energy-saving scheme minimizes copper and core losses in induction motors, which are equally applicable to induction generators. In efficiency optimization, an optimal stator angular velocity is uniquely obtained, and consequently, a corresponding optimal slip at any given rotor speed is determined. The challenge of determining a reference for rotor flux linkage that guarantees the minimal copper and core loss regime is overcome by developing a load torque observer loop. The torque observer is developed alongside a rotor speed observer for a sensorless speed operation. The observed mechanical torque is further used to enhance the outer-loop rotor speed control that generates an electromagnetic torque command used in building the reference for the inner-loop stator current control. The results obtained justified the effective operation of the torque and rotor speed observer, which consequently verified the effective minimal electrical loss regime at the optimal stator angular velocity and optimal rotor flux linkage. The results are further compared to results obtained from the equivalent induction machine drive on a finite-set model predictive control (FS-MPC) scheme with the same values of the optimal stator angular velocity and optimal rotor flux linkage. The developed efficiency-optimized vector control scheme gave lower ripples in developed electromagnetic torque and dampened overshoot better during step change in load torque. [ABSTRACT FROM AUTHOR]

This article presents a mathematical model of three-phase induction motor (IM) with a squirrel cage rotor and investigates its starting modes. Specifically, two scenarios are considered: direct starting of an IM and direct starting considering the current displacement effect in the rotor slots. Analyzing the starting modes of an IM without the use of automatic control systems is crucial for ensuring reliable, efficient, and safe operation of equipment across various industrial and commercial sectors. Understanding and accounting for the processes occurring during the starting mode of an IM allows for minimizing risks, enhancing energy efficiency, and reducing operational costs. This article details the mathematical modeling methods used for analyzing these starting modes and the results obtained from the modeling. These results were compared with data obtained experimentally, allowing for the assessment of the accuracy and reliability of the proposed model. The conducted research highlights the importance of considering current displacement in the rotor slots for accurate modeling and analysis of induction motor starting modes, particularly in capturing the differences in the amplitudes of the starting current and the faster transition to steady-state operation. Conclusions drawn from the comparison of modeling and experimental data provide valuable insights for the further development of control and operation methods for induction motors. [ABSTRACT FROM AUTHOR]

Presented in this paper is a direct model predictive control (MPC) for directly controlling the rotor speed and flux of an inverter-fed induction motor, which is also applicable to induction generators. Discrete dynamic model of the induction machine is applied in stationary reference frame with pre-set present and past reference values of the speed and rotor flux for generating the stator current references The uniqueness of the MPC proposed herein is that it does not require outer cascade proportional plus integral (PI) controllers used for regulating rotor speed and flux. Elimination of the outer loop cascade is made possible in this article by the introduction of a unique four-term cost function comprising of weighted magnitudes of the errors between four measured state variables and their reference commands. The four state variables in the unique cost function are the rotor speed, the rotor flux linkage, quadrature axis and direct axis stator currents. The MPC minimizes the cost function at every switching instance by selecting the switching states that gives the least cost function. Consequently, the selected optimal switching states are used to switch optimal inverter output voltages across the machine's stator terminals. As a precursor for obtaining an optimal rotor flux command in the MPC for every torque output, another unique efficiency improvement scheme is developed, which uniquely determines the stator angular velocity and the rotor flux that minimizes the core and copper losses while maintaining a constant slip operation. Therefore, efficacy of the proposed direct model predictive control scheme is verified by comparing its results to results obtained from equivalent vector control on equivalent machine. Results presented show lower loss regime with MPC on optimal stator angular velocity and rotor flux than vector control. [ABSTRACT FROM AUTHOR]

Multiphase machines drives have gained significant attention in the control and machines communities due to their fault-tolerant capabilities, lower power and current per phase, and high torque density. However, the difficulties in extending the current traditional three-phase regulation and control structure to multi-phase systems still limit its applicability. The performance of the Vector Control (VC) scheme using conventional PI controllers gives high total harmonic distortion (THD), reduces the system's robustness, and also large torque ripple. In this regard, this work proposes the application of fuzzy logic controller (FLC) devoted to enhancing the performance of the VC scheme of a 5-phase Induction Motor (5-phase IM) fed by a Multi-Level Inverter (MLI). This work aims to develop a control strategy that integrates the advantages of multi-phase IM and MLI using artificial intelligence algorithms, enhancing system robustness and dynamic performance. This study uses Matlab/Simulink for modeling and verifying the models' performance. The advantages and robustness of the suggested control system (VCFLC- MLI) compared to the conventional technique are demonstrated according to different tracking tests, sensitivity to changes in load torque, and robustness to changes in the 5-phase IM's parameters. The simulation results have confirmed the effectiveness of the proposed VC-FLC-MLI strategy by improving dynamic efficiency and robustness against variations in 5-phase IM parameters. As compared to the conventional strategy, the proposed VC-FLC-MLI method improves the stator current %THD and torque ripples by about 54.25% and 62%, respectively. The results show that the goals of this research were effectively achieved. [ABSTRACT FROM AUTHOR]

Motor energy efficiency has gradually become a research hotspot. In this paper, the optimization analysis of motor energy efficiency is carried out for the widely used three-phase induction motors. Based on keeping the stator and rotor structure parameters unchanged, a reasonable combination of motor steel material, winding type, and bar conductor material can realize the change in motor energy efficiency class. Firstly, the influence of stator and rotor steel materials on iron consumption is analyzed using the triple equation of iron consumption. And the loss distribution and efficiency of DW540, DW470, DW360, DW310, DW270, 1J22, and amorphous alloy materials are discussed. Secondly, the effect of different winding types on the no-load reverse electromotive force is analyzed and discussed, and its simulation model is constructed. The corresponding motor efficiency is summarized. Then, the impact of cast copper and aluminum rotors on energy efficiency is compared and analyzed. Finally, the steel material combinations, winding type, and bar conductor material are classified according to the IE3, IE4, and IE5 energy-efficiency classes. The results show that by choosing the right combination, the motor’s energy efficiency can be increased by up to 95.3%. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid technique for fault detection and classification in a three-phase induction motor (IM). The proposed hybrid technique combines enhanced ladder spherical evaluation (ELSE) and recurrent neural networks (RNN); hence, it is called the ELSE–RNN method. The main goal of the manuscript is to detect and categorize the faults that occur in the IM. The key objective of the proposed method is to enhance the performance of supercapacitor (SC) storage technology and fuzzy-tuned proportional–integral (PI) supervision over conventional control. The main contribution of this paper is developing an effective fault diagnosis method for three-phase IMs. The presence of stator, rotor, winding, and bearing faults is employed using signal processing techniques, such as the Hilbert transform and SIFT. The proposed ELSE–RNN technique is utilized with the end goal of detecting and classifying faults. Here, the proposed ELSE–RNN technique recognizes motors' healthy or unhealthy conditions in many situations to distinguish faults for protection. The proposed ELSE–RNN technique reduces the complexity of detecting and classifying faults with a validated system and increases the system's accuracy. The ELSE–RNN technique is implemented in MATLAB, and its performance is compared to existing techniques. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes open‐phase fault detection (FD) and tolerant control strategies for wye‐connected three‐phase induction machines (WCTPIMs). The FD mechanism is based on the comparison between predicted and actual values of stator currents, and the control approach is derived from the field‐oriented control (FOC). In the proposed fault‐tolerant control (FTC) method, an asymmetrical current transformation is used for stator current components, and the conventional symmetrical voltage transformation is employed for stator voltage components. It is shown that using these transformations, a modified FOC can be employed for the faulty WCTPIM. The suggested scheme is able to control WCTPIM drives under normal and single open‐phase fault (SOPF) modes with minor adjustments. Experimental results achieved from a laboratory prototype confirm the possibility and good performance of the proposed open‐phase FD and tolerant control for the WCTPIM under different operating conditions. [ABSTRACT FROM AUTHOR]

This study focuses on the motor fault diagnosis facing the long-tailed distribution data, characterized by a multitude of fault types with limited data per category and the healthy state with massive data. This skewed distribution makes the traditional diagnostic models fail to identify less frequent faults. To this end, we introduce a novel fault diagnosis model, named Transformer- and gated-recurrent unit (GRU)-based network (TransGRU), to improve the diagnosis accuracy with the long-tailed distribution data. The TransGRU has two main modules, i.e. the feature extraction module and the correction module. The former is based on the Informer encoder with ProbSparse self-attention to extract features from the long-range multi-sensor data. The latter employs the GRU network addressing the long-tail effect by adjusting the diagnosis results via the gate mechanism. Besides, we informatively design an adaptive-conditional loss (ACL) function for the long-tailed fault diagnosis by integrating the properties of focal loss, class-tailored weights, and confusion weights. ACL concentrates on challenging classifications while balancing the representation and significance of various fault modes. Validation on experimental motor data confirms the capability of our TransGRU in identifying a wide range of fault types with limited fault data compared with the Transformer and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Summary This article narrates a robust sensor‐less indirect rotor field‐oriented control (IRFOC) of a six‐phase asymmetrical induction motor (SPAIM) drive with an online estimation of parameters and reduction of common mode voltage (CMV). In comparison to the conventional IRFOC technique, the proposed IRFOC provides the least CMV using modified space vector modulation (SVM). To guarantee proper operation of IRFOC, accurate estimation of rotor time constant is mandatory; any mismatch in the actual and tuned value may lead to poor performance of the IRFOC algorithm. This problem will be further accelerated with encoder‐less control using model‐based estimators. In this manuscript, the problem of simultaneous estimation of speed and machine parameters is investigated, and a model reference adaptive system (MRAS) based speed estimator accompanied by online estimation of parameters is proposed to improve sturdiness against variation of parameters. A Simulink model of the proposed method is developed and a simulation study is carried out. To validate the simulation results, a scaled prototype model of The SPAIM rated for 2 HP, 200 V is developed and encoder‐less IRFOC for the SPAIM drives is implemented using an STM32F407VG controller board. The performance of the proposed observers is tested and verified for all the possible operating conditions and results are presented. [ABSTRACT FROM AUTHOR]

Vibration signals can effectively reflect the operating status of the motor, and are therefore considered an important basis for diagnosing induction motor faults. However, the original vibration signal has the problem of single features and long time series, and existing research usually extracts features based on expert experience, which is costly. In recent years, the accumulation of fault data has promoted the application of deep learning methods in fault diagnosis. A feature engineering method (MAC-LSTM) based on multi-attention mechanism and one-dimensional convolutional neural network is proposed for fault diagnosis of induction motors, which does not require any prior knowledge. Firstly, the multi-attention mechanism is used to expand the dimensionality of features, making the representation of original features more abundant. Secondly, the convolutional neural network extracts features from the time dimension and reduces the dimensionality, effectively solving the problem of the original signal timing being too long. Finally, LSTM captures the temporal dependence of the signal for fault diagnosis of induction motors. The experimental results show that MAC-LSTM has achieved excellent performance in fault diagnosis of induction motors based on vibration signals and has high generalization ability. [ABSTRACT FROM AUTHOR]

This manuscript addresses the critical challenges in achieving high-accuracy remote control of electromechanical systems, given their inherent nonlinearities and dynamic complexities. Traditional diagnostics often suffer from data inaccuracies and limitations in analytical techniques. The focus is on enhancing the dynamic model accuracy for remote induction motor control in both closed- and open-loop speed control systems, which is essential for real-time process monitoring. The proposed solution includes real-time measurements of input and output physical quantities to mitigate inaccuracies in traditional diagnostic methods. The manuscript discusses theoretical aspects of nonlinear torque formation in induction drives and introduces a dynamic model employing vector control and speed control schemes alongside standard frequency control methods. These approaches optimize frequency converter settings to enhance system performance under varying nonlinear conditions. Additionally, the manuscript explores methods to analyze dynamic, systematic errors arising from frequency converter inertial properties, thereby improving electromechanical equipment condition diagnostics. By addressing these challenges, the manuscript significantly advances the field, offering a promising future with enhanced dynamic model accuracy, real-time monitoring techniques, and advanced control methods to optimize system reliability and performance. [ABSTRACT FROM AUTHOR]

This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC's closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a fault-tolerant control (FTC) strategy using the current space vectors to diagnose sensor failures and enhance the sustained operation of a field-oriented (FO) controlled induction motor drive (IMD). Three space vectors are established for the sensor fault diagnosis technique, including one converted from the measured currents and the other two calculated from the current estimation technique, respectively, measured and with reference speeds. A mixed mathematical model using three space vectors and their components is proposed to accurately determine the fault condition of each sensor in the motor drive. After determining the operating status of each sensor, if the sensor signal is in good condition, the feedback signal to the controller will be the measured signal; otherwise, the estimated signal will be used instead of the failed signal. Failure states of the various sensors were simulated to check the effectiveness of the proposed technique in the Matlab/Simulink environment. The simulation results are positive: the IMD system applying the proposed FTC technique accurately detected the failed sensor and maintained stability during the operation. [ABSTRACT FROM AUTHOR]

This paper introduces a nonlinear PI controller for improved speed regulation in permanent magnet direct current (PMDC) motor drive systems. The nonlinearity comes from the exponential (Exp) block placed in front of the classical PI controller, which uses a tunable exponential function to map the speed error nonlinearly. Such a configuration has not been studied till now, thus meriting further investigation. We consider an exponential PI (EXP-PI) controller and to attain the best performance from this controller, its parameters are optimized offline using salp swarm algorithm (SSA), which borrows its inspiration from the way of forage and navigation of salps living in deep oceans. To indicate the credibility of SSA tuned EXP-PI controller convincingly, numerous experiments on speed regulation in PMDC motor have been implemented using DSP of TMS320F28335. The results obtained are also compared to similar results in the literature. It is shown that the proposed approach performs well in practice by ensuring tight tracking of the speed reference and superb torque disturbance rejection for the closed loop control. Furthermore, superior performance is achieved by the proposed nonlinear PI controller with respect to a fixed-gain PI controller. [ABSTRACT FROM AUTHOR]

Parasites possess remarkable abilities to evade andmanipulate the immune response of their hosts. Echinococcus granulosus is a parasitic tapeworm that causes cystic echinococcosis in animals and humans. The hydatid fluid released by the parasite is known to contain various immunomodulatory components that manipulate host's defensemechanism. In this study, we focused on understanding the effect of hydatid fluid on dendritic cells and its impact on autophagy induction and subsequent T cell responses. Initially, we observed a marked downregulation of two C-type lectin receptors in the cell membrane, CLEC9A and CD205 and an increase in lysosomal activity, suggesting an active cellular response to hydatid fluid. Subsequently, we visualized ultrastructural changes in stimulated dendritic cells, revealing the presence of macroautophagy, characterized by the formation of autophagosomes, phagophores, and phagolysosomes in the cell cytoplasm. To further elucidate the underlying molecular mechanisms involved in hydatid fluid-induced autophagy, we analyzed the expression of autophagy-related genes in stimulated dendritic cells. Our results demonstrated a significant upregulation of beclin-1, atg16l1 and atg12, indicating the induction of autophagy machinery in response to hydatid fluid exposure. Additionally, using confocal microscopy, we observed an accumulation of LC3 in dendritic cell autophagosomes, confirming the activation of this catabolic pathway associated with antigen presentation. Finally, to evaluate the functional consequences of hydatid fluid-induced autophagy in DCs, we evaluated cytokine transcription in the splenocytes. Remarkably, a robust polyfunctional T cell response, with inhibition of Th2 profile, is characterized by an increase in the expression of il-6, il-10, il-12, tnf-a, ifn-g and tgf-b genes. These findings suggest that hydatid fluidinduced autophagy in dendritic cells plays a crucial role in shaping the subsequent T cell responses, which is important for a better understanding of host-parasite interactions in cystic echinococcosis. [ABSTRACT FROM AUTHOR]

Motor Current Stator Analysis (MCSA) is widely used in the field of induction machine diagnostics due to its proven effectiveness in detecting various faults that can affect these machines. The broken rotor bar fault is one of the most common faults encountered in industry. This fault is characterized by its signature on the stator current spectrum, appearing as sidebands around the supply frequency at twice the slip frequency and its multiples. Indeed, despite the advantages of MCSA, it has limitations, as low-frequency torque oscillations that can induce frequency components similar to those of rotor faults when analyzing the motor current signal, which can cause false indications. This paper deals with a novel method for discrimination between broken bar fault and load torque variation of induction motors. This new method tracks the characteristic harmonic of the fault in the spectrum of the stator current envelope indicator signal. An in-depth analysis is conducted to emphasize the uniqueness of the proposal. This analysis aims to combine the Hilbert transform with the current indicator. The results obtained have showed that harmonics related to the rotor bar fault, such as 2sfs, appear around the DC component, while no signatures related to load torque variation exist on the spectrum of the stator current envelope indicator signal. The effectiveness of the proposed approach is validated through both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Electric-field energy harvesters (EFEHs) have emerged as a promising technology for harnessing the electric field surrounding energized environments. Current research indicates that EFEHs are closely associated with Tribo-Electric Nano-Generators (TENGs). However, the performance of TENGs in energized environments remains unclear. This work aims to evaluate the performance of TENGs in electric-field energy harvesting applications. For this purpose, TENGs of different sizes, operating in single-electrode mode were conceptualized, assembled, and experimentally tested. Each TENG was mounted on a 1.5 HP single-phase induction motor, operating at nominal parameters of 8 A, 230 V, and 50 Hz. In addition, the contact layer was mounted on a linear motor to control kinematic stimuli. The TENGs successfully induced electric fields and provided satisfactory performance to collect electrostatic charges in fairly variable electric fields. Experimental findings disclosed an approximate increase in energy collection ranging from 1.51% to 10.49% when utilizing TENGs compared to simple EFEHs. The observed correlation between power density and electric field highlights TENGs as a more efficient energy source in electrified environments compared to EFEHs, thereby contributing to the ongoing research objectives of the authors. [ABSTRACT FROM AUTHOR]

The voltage of the battery system is increased to increase the efficiency of the electric motor drive system. Additionally, the space vector pulse width modulation (SVPWM) technique is used to ensure high controllability. However, high-voltage and high-speed PWM switching controls for system efficiency generate high common mode voltage (CMV), and shaft voltage is induced in the bearing. This results in a shortened bearing life and potential damage. Therefore, this paper proposes a method to reduce the shaft voltage of the motor through a novel hybrid shield ring structure. It also analyzes how to improve the cooling performance of the motor using a shield ring. First, the parasitic capacitance inside the motor is analyzed. Then, the shaft voltage reduction technology is analyzed according to the material of the shield ring. Finally, experiments validate the proposed method. Additionally, the temperature characteristics of the main part of the motor are analyzed through an experiment in consideration of the shield ring. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI). Design/methodology/approach: The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control. Findings: The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller. Originality/value: The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions. [ABSTRACT FROM AUTHOR]

Currently, power trains based on an Open-End Winding Induction Machine fed by a Dual Inverter (DI-OEWIM) are attracting a great deal of interest in various modern industrial applications. However, applying symmetrical control to this system (DI-OEWIM), which is symmetrical in nature, will lead to malfunction. Therefore, the objective of this paper is to explore the influence of asymmetric control on the performance of this system. The principle of this study is to create an asymmetrical control by integrating a phase-shift angle in the Space Vector Pulse Width Modulation (SVPWM) strategy. We then evaluate the impact of these angles on various performances, such as the Total Harmonic Distortion (THD), power losses, Common Mode Voltage (CMV), Zero-Sequence Voltage (ZSV), rotation speed and torque ripple of this system. This study was carried out in the Matlab/Simulink environment and was validated experimentally using the dSPACE 1104 board. The results show that the different angles have significant effects on the overall performance of this system. [ABSTRACT FROM AUTHOR]

Electrical equipment plays a vital role in industry. Among various electrical equipment, induction motors are quite commonly used in many industrial applications. One of the most common faults that occurs in induction motors is bearing fault. In this article, bearing fault is diagnosed in an induction motor using vibration signals with the help of a simple Artificial Intelligence (AI)-based model. Because, the vibration signals are not dependent on the motor type, simple to measure, cost effective and yields good results. In the proposed system, accurate prediction of bearing condition is carried out using Bayesian optimization-based ensemble classifier (BOEC). The performance of the BOEC-based bearing fault diagnosis system is compared with other conventional techniques and the comparison results confirm the superior performance of the proposed system. Also, the accuracy obtained from the BOEC-based bearing fault diagnosis system is 99.97%. To verify the effectiveness of the proposed system, a hardware prototype is set up in the laboratory and bearing conditions of various induction motors are analyzed. [ABSTRACT FROM AUTHOR]

The paper presents a nonlinear field-oriented control technique based on the tensor product representation of the nonlinear induction machine model and the solvability of linear matrix inequalities. The nonlinear model has 32 quasi linear parameter-varying equivalent variants, and it is shown that only half of the models result in feasible controller. Two control goals are realized: torque control and speed control. The controller is a nonlinear state feedback controller completed by integral action. A new block diagram is investigated for speed control. The controller gains are designed by the solution of linear matrix inequalities to solve the Lyapunov inequality to obtain a stable and fast response and constraints on the control signal. The presented methods are verified and compared by simulations. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present an investigation of a small deep learning model applied to the detection of a broken rotor bar of an induction motor. The motor current spectrum analysis is the base method for fault detection. This proposed method focuses on the analysis of the modification of the input vector and model configuration. This method was implemented and it showed that the feature length and size of the model are reduced compared with the existing method. The experimental results showed that only feature extraction using the spectral-based method and limit range of its coefficient are adequate to provide accuracy of small deep learning comparable to that of the parallel-layer deep learning model. Likewise, at the same accuracy level, based on the deep learning model, a shorter sampling duration than that required by the reference model is needed. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines. Design/methodology/approach: The experimental study involves inducing short-circuit winding turn variations on the induction machine's stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity. Findings: The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults. Research limitations/implications: While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis. Practical implications: The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies. Originality/value: This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation. [ABSTRACT FROM AUTHOR]

When performing bearing fault measurements, the unbalanced magnetic pull's (UMP) influence is inadvertently incorporated. The UMP's influence distorts the measurements used for bearing fault size estimation, leading to inaccurate fault interpretations. In this paper, we combined a magnetic equivalent circuit motor model with a dynamic bearing model to isolate the UMP's effect, which is unprecedented in the state of the art. The coupled model is used to analyse the dynamic interaction between a mechanical fault and the UMP without neglecting the influence of the rotor slot harmonics nor the magnetic potential losses in the stator and rotor. We compare simulations performed with the UMP and without the UMP in the presence of (i) an outer race bearing fault and (ii) an outer race bearing fault combined with an intrinsic rotor misalignment (IRM). The study finds that the UMP amplifies the rotor eccentricity by a non-negligible amount without accounting for an IRM. Moreover, with the inclusion of an IRM, the rotor vibrations are distorted to the point where bearing fault estimation algorithms could become inoperable. The presence of rotor slot harmonics in the UMP has been identified as a key contributor to the distortion. The findings presented in this article can be used to improve bearing fault size estimation algorithms by isolating the disturbances caused by the UMP. [ABSTRACT FROM AUTHOR]

The predictive current controller has arisen as a practicable technique for operating multiphase machines due to its fast dynamic response, control flexibility, and overall good performance. However, this type of controller has limitations, e.g., it tends to suffer from steady-state tracking errors in ( d − q ) currents; high computational burden; and high ( x − y ) currents, which become more pronounced at higher speeds, thereby worsening its sustainability. While some proposals have addressed these limitations by incorporating modulation stages and new cost functions, there is still room for improvement, particularly at higher speeds. In line with the pursuit of sustainable advancements, this article explores the integration of a field-weakening strategy with a modulated predictive current controller applied to a six-phase induction machine to improve its performance at current tracking for higher speed ranges. Experimental tests were conducted to validate the effectiveness of the proposed controller, assessing stator current tracking, reduction in the ( x − y ) currents, and the total harmonic distortion. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on analyzing asymmetric and open-phase fault modes in symmetrical two-channel six-phase induction machines while considering the spatial harmonics of the electromagnetic field in the air gap. Under sinusoidal power supply, the magnetomotive force exhibits high-order spatial harmonics, which arise due to the winding design. The interaction between these spatial harmonics and the first-time harmonic of the power supply results in the presence of high-order harmonics in the current and electromagnetic torque. The harmonic content of the currents and torque under asymmetric and open-phase operation mode while taking into account spatial harmonics is analyzed. It is shown that in asymmetrical modes, in addition to the 5th, 7th, 11th, and 13th higher harmonics found in symmetrical modes, harmonics in multiples of the 3rd are introduced into the stator winding currents. As for the composition of the electromagnetic torque in asymmetrical modes, all even harmonics are present, in addition to the 6th and 12th harmonics that are characteristic of symmetrical modes. A mathematical model of the six-phase induction machine has been developed using the average voltages within the integration step method. Its adequacy was verified by comparing the simulation results with the experimental results obtained from the developed prototype. [ABSTRACT FROM AUTHOR]

This paper presents a voltage control system for multiphase squirrel-cage induction generators operating at a high variability of speed and variable load. Field-oriented vector control was used with a change in the sequence of the stator phase currents what changes the number of poles of the magnetic field produced by nine-phase stator winding. At low speeds, the current sequence is changed so that the number of poles increases allowing for the desired voltage to be obtained with greater efficiency. The task of the automatic control system was to control the DC voltage to a desired value at the output of the multiphase PWM converter. This is an alternative control method to the scalar control of voltage and frequency presented in a previous work. The control method and parameters of the automatic control system result from the mathematical model of the multiphase induction machine. The results of the laboratory tests were compared with the effects of the operation of the same nine-phase scalar controlled generator. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is introduced in systems with electrical machines, such as in electric drive systems, wind energy generating systems, and small and special machines, to remote monitor and control the operation for data acquisition and analysis. These systems can integrate with the equipment and retrofit the existing installations. At the end of the control loops there are always motors, or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion. This article analyses selected aspects of research and applications of IoT-based control in electric drive systems and of wind energy conversion systems with induction machines. Various applications and study cases of control systems of electrical machines with IoT technology are described. With the IoT-based control of induction machine systems operators can remotely monitor parameters and obtain accurate real-time feedback during fast changing duty cycle operation. Thus, IoT creates multipurpose instruments in the remote control of induction machines. The paper offers a comprehensive analysis of IoT-based control applications in the field of induction machines, with technical details of design, construction, experimental testing, and prototyping, that are useful to energy engineering specialists in the sector of electric drives and wind energy conversion systems. [ABSTRACT FROM AUTHOR]

Predictive maintenance is required for Induction Motor (IM) to avoid sudden breakdown. In this paper, multimodal sensor data are used for prediction of the rotor slot width variation during runtime of IM. Moreover sensor data are visualized through visual image obtained using Scale Invariant Feature Transform (SIFT) and matching dictionary for various faults in motor such as overload, high speed, Broken Rotor bar (BRB), and unbalance magnetic pull. The multimodal sensor signals data acquired from different parts of three phase induction motor are analysed using various transforms such as Over Complete Rational Dilation Wavelet Transform (ORaDWT), Tuneable Q Wavelet Transform (TQWT), Polynomial Chirplet Transform (PCT) and Dyadic Wavelet Transform (Dyadic WT) for various fault conditions and rotor slot width mentioned during runtime condition of motor. The rotor slot width is predicted using Multiple Linear Regression (MLR), Polynomial Regression (PR), Logistic Regression (LR) and Soft-max Regression (SR) methods through the mean value and energy band of the acquired sensor signal. The Dyadic WT and SR perform better for rotor width prediction. The proposed method such as Dyadic WT and SR provides the prediction accuracy of about 95.2%. From experimental results the rotor slot width expansion more than 2% needs immediate attention to avoid breakdown of motor. [ABSTRACT FROM AUTHOR]

We present an analytical model of permanent-magnet five-phase synchronous machines including the third space harmonic of the airgap induction, which is generally disregarded by most authors, despite its relevance for the estimation of the machine behavior. Firstly, we address the numerical estimation of the self and mutual inductances required by the model using the machine dimensions and winding characteristics. Afterward, we derive a model in the stator reference frame, which is subsequently simplified through complex coordinate transformations, thus resulting in a set of simpler, decoupled equations expressed in the stator reference frame. Then, to test and validate the model, it was applied to two prototype machines available in our laboratory. We designed the rotor of these two machines so that each has a different amplitude for the third harmonic of the airgap induction. Finally, to further validate the model, analytical results were compared with corresponding results obtained through finite element analyses and also through practical experiments, from which an excellent agreement emerged between the three types of results. The results obtained with the finite element method and through experiments also proved not only that the model is consistent and valid, but also that it can be used to predict the machine behavior under normal and faulty operation. [ABSTRACT FROM AUTHOR]

The main aim of this article is to achieve predictive maintenance by proposing reliable residuals specific to outer ring bearing faults. Our work falls within the general context of maintenance, with particular emphasis on vibration analysis techniques. For an in-depth study of the fault, we use spectral analysis of electromagnetic torque, which gives very satisfactory results compared with work based on line current or neutral voltage fault signatures due to the similarity in the mechanical nature of the fault and the signal to be studied. In fact, an analytical calculation of the electromagnetic torque has been developed to obtain the specific frequencies of the fault under consideration. To highlight our results, the simulation of the analytical calculation of this fault was implemented in MATLAB/SIMULINK using the Fast Fourier Transform method to extract fault signatures. The induction motor's performance was analyzed under various operating conditions, including both healthy and faulty states. Finally, an experimental study will support our analytical developments. [ABSTRACT FROM AUTHOR]

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Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm's effectiveness and robustness. [ABSTRACT FROM AUTHOR]

The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque. [ABSTRACT FROM AUTHOR]

Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Owing to the induction machine's widespread use across most industries, engine failure will be quite expensive. To address this problem, numerous signal processing techniques have been adopted. This work proposed a novel technique called GLTP dependent on local ternary patterns for texture analysis to identify defects in induction motor with the grey level co-occurrence matrix (GLCM). This technique is compared with another original technique named GLKTP based on local ternary pattern using Kirsch operators used to show eight main directional changes in the image and combined with the GLCM matrix for texture analysis. A set of acoustic data is employed with different engine failures (bearing defects and broken bars). The multiclass MCSVM (Support Victor Machine One vs. All) and K-NN (K-nearest neighbourhood) and ANN artificial neural network classifiers are utilized for fault identification. [ABSTRACT FROM AUTHOR]

Ball bearings fault detection with the aid of ultrasound technique is of great importance. Hence, this paper evaluates signal processing of ultrasounds sent by faults and addresses relevant challenges. In this regard, the waveforms of ultrasound signals were analyzed for different types of faults and considering specification of ultrasound channels to enhance the perception of ultrasound signal processing. For this purpose, different conditions have been considered such as waveform fading and the likelihood of frequency fading at lower signal-to-noise ratios. Furthermore, the sensitivity of ultrasonic sensors to fault detection at higher frequencies and the probability of the modulation have been assessed, and modeling results have been presented. In addition to modeling, fault detection in a ball bearing of an electromotor has been studied through an industrial-empirical case study. Therefore, the bearing signals have been processed for fault-free, initial fault, and advanced fault conditions. The studied fault emerged in several months (without hand intervention) and was then intensified over time. According to results, the envelope technique is capable of extracting fault frequencies of ball bearings. Also, high-pass filters have been employed to demonstrate signal shape mode and impulse generation in detail. Moreover, it has been shown that the growth of initial faults can generate new frequencies and fault frequency harmonics other than the fault frequencies of ball bearings. Also, important tips on the ultrasound fault detection of electromotor were provided with a potential effect on the entire fault detection process. These tips came from the author's experiences obtained by monitoring rotary machines. In which, they can serve as a beneficial tool for monitoring of such machines in industrial sites. [ABSTRACT FROM AUTHOR]

In this paper, the performance of different continuous-time and discrete-time models of the electrical subsystem of induction machines and permanent-magnet synchronous machines as well as methods based on them for decoupling the direct and quadrature axis components of the stator current are investigated and compared. The focus here is on inverter-fed, pulse width modulated drives when operated with a relatively large product of stator frequency and sampling time, where significant differences between the models and decoupling methods used come to light. Recommendations for a discrete-time model to be used uniformly in the future are made, as well as statements on whether feedforward or feedback decoupling structures are better suited and whether state controllers improve decoupling measures for very steep speed ramps. Simulation studies and measurement results support the statements made above. [ABSTRACT FROM AUTHOR]

Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application. [ABSTRACT FROM AUTHOR]

This paper presents the structure of a single-phase motor powered by a 50/150 Hz magnetic frequency converter and selects the arrangements of its windings to obtain favourable parameters. A physical model of the motor is described and its mathematical model is proposed, which has been adapted and realised in the PSpice application. The results of the computer simulation are provided in the form of characteristic curves of the speed and the electromagnetic torque as a function of time. [ABSTRACT FROM AUTHOR]

The paper's subject is the analysis of the operation and synthesis of the control of an induction motor test bench, which is to be used to test motors with rated powers up to 3 MW and rated voltages up to 15 kV. The tested motor and the loading induction generator are powered by inverters implementing the scalar control of both machines. The inverters enable smooth adjustment of the motor power supply and its load. The load control algorithm allows its automatic variation in the set range, which, combined with the automation of measurements, reduces the involvement of technical personnel in the tests. However, the cooperation of both machines with inverters raises specific technical problems related to the stability of the system, especially in the case of low loads of the tested motor. The paper presents solutions to these problems, an appropriate algorithm for controlling the load generator, and the results of simulation tests of the control system. [ABSTRACT FROM AUTHOR]

This paper describes step by step the modelling and simulation of the open loop scalar command for a three-phase induction machine with known parameters, which operates in motor regime. In order to change the electromagnetic torque and implicitly the rotor speed, in this paper the solution of applying a positive mechanical torque from outside the machine was chosen, without dynamically adjusting the supply voltage. The chosen mathematical model for the induction machine is the one described by Krause's equations, written in a stationary reference frame. The simulations were done on the Matlab/Simulink simulation environment and the behaviour of the machine was monitored under the working conditions listed above. [ABSTRACT FROM AUTHOR]