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Early fault detection for the induction machine became a necessity to prevent the escalation of failures to severe levels, thereby avoiding unscheduled downtimes. Among the various failure modes in electrical machines, rotor-related faults, such as air gap eccentricity, require particular attention and to detect this type of defects model-based methods are extensively used in this field. However, because of the intricacies of the diagnosed model and the time-consuming investigations it renders the diagnosis process more laborious and less efficient. This article focuses on applying a non-model based approach that relies in general on feature extraction using discrete wavelet transform decomposition analysis of stator current signal for various stages of air gap eccentricity and under multiple operating conditions and as a first step of the conducted work, through performing an in-depth energy distribution analysis through all of the decomposed signal levels to extract the best sub-signal level that holds the most relevant information about the machine's condition alongside to RMS values of the signal. The second part of the research focuses on employing the extracted features as input data used for training a multi-layer perceptron algorithm such as support vector machine and decision trees. Our endeavor is to choose the most accurate algorithm for the multiclass classification. [ABSTRACT FROM AUTHOR]

This paper employs artificial intelligence to diagnose induction machine health by detecting air gap eccentricity under varied conditions. It addresses Model-Based Method and conventional MCSA techniques limitations, requiring extensive model knowledge. The proposed technique relies on stator current signals, simplifying data acquisition. Using Root Mean Square and raw data using the three phases of stator current from a multi winding model of a squirrel cage induction machine. The study emphasizes on employing classification and regression tasks for supervised learning as a non-model-based approach by applying several models and classifiers to choose the best one for the monitoring task. This approach allows online diagnosis, detecting defects early, even under weak load conditions by conduction a multiclassification technique for each class of the dataset. The paper's strength lies in its holistic analysis of signal fluctuations, categorizing faults based on nature and location. Overall, the proposed algorithm for the classification which is Decision Trees achieved an overall accuracy surpassing 80% against other classifiers, and for the regression task Random Forest outperformed by delivering the least values of loss error with 0.014 using mean square error evaluation metric and achieving a 98.6% accuracy. [ABSTRACT FROM AUTHOR]

This paper deals with the parameter identification of induction machine rotor and stator windings. These two magnetically coupled circuits are described and analyzed to explain the novel method that can identify the set of substitute parameters (not equivalent circuit parameters) which can be used for some analysis of transient processes in a stationary induction machine. The parameters are: inductive leakage factor, substitute rotor resistance, substitute rotor inductance, and substitute mutual inductance. The method belongs to the group of so-called off-line methods, i.e., it is performed on a machine in a standstill, and it assumes a star-connected induction motor. The correctness of the method is verified by calculation as well as by measurement. Moreover, the practical use of the method is shown in an example of how to determine the rotor winding temperature rise, and the results of the measurement on an induction motor with a power of 0.75 kW are presented. The method can be performed even when the rotor circuit is hidden inside the completely closed rotor construction, as in the case of a squirrel cage induction motor, i.e., without access to the rotor circuit. [ABSTRACT FROM AUTHOR]

A new predictive model reference adaptive system (MRAS) speed estimator was proposed to address the problem of the traditional proportional-integral (PI) adaptive rate in the induction machine ( IM) drive system used for shield tunneling without a speed sensor. The traditional PI adaptive rate in the MRAS system relies on manual experience adjustment and cannot adapt to model changes. The proposed estimator was based on the principles of finite control set model predictive control. A search-based optimization algorithm was adopted during the rotor speed calculation, which ensures minimum speed adjustment error signals in each sampling period and avoids the use of a proportional integral controller. Experimental results show that the proposed speed estimator has excellent dynamic and static performance over a wide speed range. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of corrosion on the magnetic circuit's influence on the induction machine's magnetic characteristics. To achieve this objective, we initially evaluated the magnetic properties of the induction machine's magnetic circuit under both corroded and non-corroded conditions by analyzing hysteresis loops using a vibrating sample magnetometer. We systematically examined magnetic parameters extracted from these experiments, including saturation magnetic flux density, coercive field, remanence, and the squareness ratio. Subsequently, we employed the finite element method to scrutinize the magnetic behavior of the induction machine under both corroded and non-corroded conditions. The magnetic parameters obtained from the experiments were then incorporated into the model representing the induction machine's magnetic circuit. The results demonstrate that corrosion in the magnetic circuit significantly impacts the induction machine's magnetic behavior, ultimately leading to reduced machine efficiency. [ABSTRACT FROM AUTHOR]

Electrical machines that can run at high speeds are more and more studied as they can respond to the increasing need of power onboard of aircrafts. However, to allow high-speed operability mechanical handling of the rotating parts need to be insured. In this paper an analytic design process of a novel high-speed induction machine is presented. The analytical magnetic and mechanical models developed are presented and validated with finite element simulations. The magnetic model is based on a classic equivalent electrical diagram of induction machine with a specific adaptation for the rotor leakage inductance as the squirrel cage is buried. The mechanical model is based on a field displacement approach leading to the stress tensor in all the rotating part. A four degrees of freedom vibration analysis model considering gyroscopic effects based on Euler–Lagrange equation allows to identify the critical speeds of the system. It is shown that some geometrical parameters will have opposed effects on the two physics. Thus, an optimization-based coupling between the different physics allows to design rapidly the desired machine regarding any technical specifications as analytical models are being used. [ABSTRACT FROM AUTHOR]

In this paper, a machine learning based approach is presented for detection and classification of faults in an induction machine. Five different classification algorithms, namely, support vector machine (SVM), decision tree, random forest, naive Bayes, and extreme gradient boosting (XGBoost), are adopted. The diagnosis of the most common types of faults such as broken bars, interturn fault and outer racing fault are considered. The current signatures under healthy and various faulty conditions are used for training and validating the models. The feature extraction step is implemented with the help of discrete wavelet transform (DWT). Following DWT, the features obtained are fed to the classification algorithms and subsequently the performance of each algorithm with respect to each fault condition is evaluated with appropriate metrics. Finally, a performance comparison is done and the most suitable classifier for reliable diagnosis of each of the fault condition is suggested. [ABSTRACT FROM AUTHOR]

The accurate and time-saving prediction of essential machine variables (output power, torque, and efficiency) is crucial for manufacturers offering a wide power range of induction machines. Many motor variants are typically produced by axially scaling and rewinding the machine. Rescaling procedures of electromagnetic models of induction machines are in everyday use and well known. However, while a high accuracy can be achieved by rescaling in theory, more significant deviations between simulated and measured output parameters of the realized scaled device occur in praxis. These deviations can mainly be attributed to the faulty separation of effects in the distinct machine components, such as the rotor, stator, and bearings. This paper introduces an optimization-aided modeling approach based on the induction machine’s simple equivalent circuit representation. The method is validated by measurement data obtained from many induction machines with various axial lengths and winding configurations. [ABSTRACT FROM AUTHOR]

Induction machines are widely utilized in the industry due to their sturdy construction nature and relatively simple maintenance. For the design, control, optimization, and failure analysis procedures of induction machines, equivalent circuit parameters are required. These parameters can be estimated using a variety of techniques, such as traditional testing, experimental research, analytical methods, programming, and machine learning algorithms. However, the aforementioned methods have limitations; experimental methods are laborious and time-consuming, analytical methods require complicated and iterative computations for each machine, and programming algorithms involve writing of lengthy command sequences. The article concentrates on a high precision estimation method based on neural network algorithms to avoid complex calculations, thus, minimizing time and effort, and eliminating errors. In this context, it is aimed to provide a greater reliability compared to studies using small amounts of datasets and to investigate earlier methods, which are not studied vastly, for estimating the parameters of induction machines. In this regard, three distinct artificial neural networks were applied to a large dataset consisting of 1164 machines with power output ranging from 4 to 900 kW and belonging to 7 different manufacturers. RMSE values of 0.0125 were attained in parameter estimation, and artificial neural networks produced encouraging results. [ABSTRACT FROM AUTHOR]

Linearization is a powerful tool that enables the analysis and control of nonlinear systems by simplifying their dynamics and allowing the use of well-established linear control techniques. This paper presents a new methodology for the linearization of dynamic nonlinear systems that can be applied without the need to know their internal structure and parameters solely from measured input/output data. The proposed technique, based on fuzzy logic, simplifies a higher-order nonlinear system into a first-order nonlinear fuzzy system. This enables the design of control systems based on an inverse fuzzy model in the vicinity of the chosen operating point. The properties of the proposed methodology were first verified for less complex systems by simulation in MATLAB. This paper presents the application of this method for torque control of an induction motor, which is a complex nonlinear system of the fifth order. The results were experimentally validated by measurements using an IC inverter-induction motor system. The experimental measurements confirmed the correctness and quality of the proposed methodology. Compared to traditional methods, it does not require any knowledge of the mathematical model of the nonlinear system, use of complex differential algebra, or other additional transformations. The proposed non-analytical linearization technique is primarily designed for a class of mechatronic systems with electric drives. Its simple structure and design methodology minimize the complexity of implementation, thereby enabling it to be widely used in industrial practice for other nonlinear systems with comparable structures.

The significant occurrence of bearing faults in electrical machines necessitates continuous online monitoring of the machine’s operating data with the main objective of ensuring both high reliability and efficiency and therefore minimising the chance of unwanted breakdowns. This work focuses on the simulation of (defective) bearings, utilising a dedicated model with five degrees of freedom (DOF) (translational motion) in conjunction with an induction motor model. The primary objective is to gain a comprehensive understanding of how faulty bearings influence both the entire bearing itself and the machine, mainly concerning vibration signals and additional frictional torque. Additionally, various shapes of spalls on the raceway(s) are described, analysed and compared. This work is an extended version of the conference paper ‘Simulating Rolling Element Bearing Defects in Induction Machines’, presenting additional information on how to simulate spalls (with different shapes and sizes) on the inner ring of the bearing. Furthermore, the so-obtained vibration signal is examined and a method is proposed aiming to verify the simulation results and to predict the location of the spall (raceway of the inner or outer ring).

Induction motors, known for their reliability and efficiency, are widely used in various industrial applications. However, their susceptibility to failure, particularly in harsh environments, poses significant operational challenges. Early detection of faults is essential to avoid unplanned downtime and costly repairs. In this study, we propose a novel diagnostic approach derived from the analysis of four distinct types of harmonics: (i) Rotor Slot Harmonics (RSH), (ii) time harmonics (TH), (iii) eccentricity fault harmonics (EFH), and (iv) rotor bar fault harmonics (RBFH). These harmonics are examined through fast Fourier transform (FFT) analysis, enabling the identification and characterization of various fault types. Experimental results indicate that TH appears to be the most sensitive harmonic for detecting stator faults, with a residual fault detection value of 4.5 A at full load and 5.05 A at half load. Real-time FFT processing of stator current signals, compared to a healthy reference signal, gives rise to the advanced residual optimal mapping (AROMA) approach. This approach allows for accurate detection and severity assessment of stator faults, where fault severity values are measured at 33.65 A for full load and 17.24 A for half load. Our innovative strategy seeks to utilize the remaining differences among the harmonics displayed by the established healthy state stored in the database and those detected in the real operational healthy state. This enhanced sensitivity and precision in fault detection and severity assessment aim to significantly reduce unplanned downtime and associated costs.

The development of the novel multiphase brushless doubly-fed generator system and voltage controller for stand-alone mode configuration is proposed in this paper. The generator system is based on the new machine construction with multiphase control winding and traditional three-phase power winding. The dynamic model of multiphase brushless doubly-fed generator is presented, and the control strategy for voltage amplitude and frequency is developed. The simulation and experimental results are used to validate the performance of the topology of the multi-phase brushless double-fed generator and of the control system. The tests of the generator system have been carried out for the normal operating state of the system and for the case where one of the control phase is inactive. This situation is equivalent to a failure in the power path of the control winding.

Magnetic decoupling principle when applied to electrical machines states that if two windings are configured for different number of pole pairs, they would not interact with each other magnetically even though they share a common magnetic core. This principle forms the basis for developing special machines where two or more machines can be integrated with the same magnetic circuit. This paper deals with formulating the mathematical analysis that determines the validity of this principle during practical conditions (i.e. non-sinusoidal winding distribution, flux saturation, etc.). Extensive Finite Element Method (FEM) simulation results from the Ansys Maxwell-2D software platform closely obey the conclusions derived from the mathematical analysis. As an example, new brushless and magnetless synchronous machines (SMs) have been developed by using this principle. It is designed by embedding an induction machine (IM) with a SM. Experimental investigations conducted on the laboratory prototype support the mathematical analysis dealt with in this paper.

Identifying stator imbalance problems in induction machines (IMs) is crucial to preserving operating efficiency, reliability, and safety. This identification contributes to the IM’s overall health, prolongs its lifespan, and helps organizations meet regulatory and performance standards. Moreover, it enables the execution of maintenance tasks when required, improving resource use and decreasing shutdown periods. This research presents a Non-Destructive Technique (NDT) that utilizes a thermography-based machine learning algorithm for diagnosing Stator Imbalance Defects (SID) within IMs experimentally. This technique relies on assessing temperature distribution on the outer surface of the IM stator. Then, the evaluation of various statistical variables that provide valuable insights into the normality of temperature distribution within the designated area referred to as the Region of Interest (ROI) is presented. Initially, thermal images of the stator are captured under various operating conditions, including a healthy state, SID presence, and phase missing. Then, all thermal images are converted into thermographic data matrices for statistical analysis using the Machine Learning Algorithm Gaussian Naïve Bayes (GNB). This analysis aimed at classifying SID in IMs arising from deviations in the normalcy of temperature distribution within the ROI. To demonstrate the efficacy of the suggested GNB approach, a comparison with the Subspace Discriminant Classifier and Cubic Support Vector Machine is provided. The GNB algorithm exhibits exceptional accuracy of 95.2% in classification tasks while maintaining a minimal processing time, irrespective of the operational duration of the IM.

Integration of the impacts of time and space harmonics simultaneously during the design stages of inverter-fed induction machines (IMs) is crucial for the accurate calculation of electromagnetic (EM) torque and ripples. Traditional magnetic field-based models offer an analytical approach for determining the EM torque in IMs by calculating inductances. However, by assuming infinite core permeabilities, these models neglect the impact of the core magnetomotive force (MMF) drops due to the difficulties in accurately calculating these drops and the impracticality of isolating the contribution from each phase, which is essential for inductances calculations. These factors contribute to deficiencies in this modeling approach, which become more noticeable when the combined impacts of time and space harmonics on MMF drop calculations are also disregarded. Therefore, this paper introduces a novel magnetic-field-based model to predict the torque and torque ripples of inverter-fed induction motors by addressing the above limitations. This involves modifying the turns and winding function, and calculating the core MMF drops based on the timely variation of non-sinusoidal core flux densities, considering their major and minor flux-density loop effects. Consequently, the associated energy is used to calculate the net available energy, thereby enhancing torque calculations. Compared to the experimental results obtained from an 11kW prototyped induction motor, the proposed model exhibits notable enhancements, achieving average accuracies of 96.4% for average torque and 94.51% for torque ripples, in contrast to the respective traditional model accuracies, 81.1% and 45.1%.

In traditional motor current signature analysis (MCSA) approach, the spectral leakage of the fundamental supply frequency can obscure the characteristic fault component under low-load or no-load conditions. Furthermore, most time-frequency (TF) methods often have low resolution and are not qualified to produce a narrow band in the output. In this paper, we employ multi-synchro-squeezing transform (MSST) to show its effectiveness in fault detection of induction machines, for the first time. The key innovation of this work is merging MSST (due to its high time-frequency resolution) with the third-order energy operator (TOEO) (due to its high accuracy in fault detection). Specifically, TOEO is used to overcome the leakage effects of the supply frequency, through a demodulation approach for asymmetric fault detection along with MSST technique. The proposed method was evaluated for induction machine fault detection in both steady-state and transient conditions. Both analytical and experimental results confirm that the proposed method can excellently reveal the fault characteristic frequency in steady-state and transient mode, instead of the sideband frequencies.

Induction machines (IMs) are used extensively in irrigation systems due to their high reliability and line-starting capability directly from the power grid without requiring variable speed drives (VFDs). Nonetheless, line-connected IMs draw a substantial starting current from the grid, leading to grid instability, heightened demand charges, and premature failures in irrigation systems due to excessive water pressure. Although VFDs can effectively alleviate the starting current, their associated costs for equipment, installation, and maintenance are higher than line-connected IMs. Consequently, this results in limited adoption in the market. This paper proposes a shunt-connected photovoltaic (PV) inverter with a newly devised synchronous scalar control technique to tackle this issue. This system can be retrofitted into existing line-connected IM setups to reduce the excessive initial current effectively. Once the IM attains its rated operating state, the power source is seamlessly switched from the PV inverter to the power grid within two electrical cycles. This minimizes transient currents while ensuring uninterrupted operation of the irrigation system. The PV inverter can supply continuous active and reactive power, resulting in energy savings throughout the year during irrigation and off-irrigation seasons. Moreover, the proposed PV inverter can be dimensioned as small as 50% of the irrigation system, significantly reducing the peak starting current. Simulation and experimental results closely align, demonstrating the favorable soft-starting performance of the inverter.

This paper describes the detailed design procedure of electric machines using finite element analysis (FEA). The proposed method uses the available findings from the literature and FEA results for the design procedure. In addition to electromagnetic analysis, thermal analysis is executed to examine the capability of the designed machines for handling the load in terms of thermal limits. It allows for considering the normal and overload performance of the electric machines during design. The proposed design procedure is used for designing a 100 kW induction machine (IM) and interior permanent magnet synchronous machine (IPMSM) for a parallel hybrid electric vehicle (HEV). The differences between the performance parameters of the studied machines are discussed, and the advantages and disadvantages of each design are highlighted. The designed machines are compared with commercially available electrical machines in terms of performance and power density. The comparison demonstrates that the developed machines can offer comparable performance to other designs.

Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control. [ABSTRACT FROM AUTHOR]

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]

Induction machines have become the standard for highly demanding industrial applications. This has led to the utilization of modern discrete-time control techniques (such as model predictive control) that require the estimation of internal variables that are not subject to measurement (such as the rotational velocity in sensorless applications). From this point of view, it is fundamental to have accurate discrete-time models of induction machines, particularly given their nonlinear nature, so that control techniques perform according to design requirements. In spite of the above, the modeling of induction machines has not received much attention in the literature, even though more powerful machines and faster microcontrollers are currently being used. To better understand induction machine models for control, in this paper, we develop and compare various discrete-time models of induction machines based on Euler, Taylor, and Runge–Kutta methods. In addition, we compare the Extended Kalman Filter and Unscented Kalman Filter for state estimation in terms of accuracy and computational burden. The models are derived and compared through extensive Monte Carlo simulations and the state estimation techniques are compared in terms of root mean squared error, execution time, and maximum absolute error. Our simulations show that, in general, the Taylor method yields more accurate models than both the Runge–Kutta and Euler methods. In particular, the Taylor method results in a root mean square error that is one order of magnitude smaller than the Euler method for stator current and rotor flux linkages. For rotor angular speed, the Runge–Kutta methods are more accurate than both the Taylor and Euler methods, resulting in a root mean square error that is two orders of magnitude smaller than the Euler method. On the other hand, the Extended Kalman Filter results in smaller execution time than the Unscented Kalman Filter, up to two orders of magnitude. In terms of root mean squared error and maximum absolute error, both filtering algorithms perform similarly. [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]

This research examines how fluid damping loss affects the operation of a two-pole, 5.5 HP (4 kW) induction machine (IM) within the context of different slot opening configurations developed for downhole water pump applications. Since these motors operate with their cavities filled with fluid, the variations in fluid viscosity and density, compared to air, result in the occurrence of damping losses. Furthermore, this loss can be attributed to the motor's stator and rotor surface geometry, as the liquid within the motor cavity moves unrestrictedly within the motor housing. This study involves the examination of the damping loss in a 24-slot IM under different stator slot indentations. The investigation utilizes computational fluid dynamics (CFD) finite element analysis (FEA) and is subsequently validated through experiments. The aim of this work is to emphasize the significance of fluid damping loss in submerged machines. Results reveal that the damping loss exceeds 8% of the motor output power when the stator surface has indentations, and it diminishes to 3.2% of the output power when a custom wedge structure is employed to eliminate these surface indentations. [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]

The paper presents the possibility of using ERP business software for the purpose of analyzing steady states of electric drives. A centrifugal pump is used as an example of active load. After reviewing previous research and presenting the basic principles of using the ERP model, the analysis of the operation of the centrifugal pump and the processing of the results of the propulsion measurements were approached. Two flow control options are considered and both methods are compared regarding energy consumption. After that, the method of calculating energy saving using the ERP model is shown. The calculation results provide data on different costs depending on the type of flow control. Since an induction motor with a frequency converter is provided for flow control by changing the speed, the final step is rough profitability estimate of the investment in frequency converter, which was also done directly in the ERP model. [ABSTRACT FROM AUTHOR]

Direct Torque Control (DTC) of Induction Motors (IMs) is popular in motor drive applications because of its robust and simple control structure. The IM winding can be controlled on both sides using dual inverter technique which more effective for Electric Vehicle (EV) with a greater number of voltage vectors. However, the battery performance of the dual inverter will deteriorate unevenly on both sides, resulting in fluctuating voltages for the EV system. This will lead to the generation of distorted stator currents and a significant droop in the stator flux, which in turn can increase the total harmonic distortion (THD) in the system. Additionally, the performance of torque may not be able to regulate effectively. This paper examines the effect of unstable voltage on voltage vector mapping performance with tilted angles and proposes new sector definitions based on voltage ratio conditions. Moreover, the proposed sector for each predefined voltage ratio is tested under three-speed conditions. The proposed technique effectiveness is validated through hardware experiments using a dSPACE 1104 controller and retuning the stator current for proper waveform. This approach improves the stator current waveform, improves stator flux droop, enhances torque regulation and minimizes the THD in the DTC system.

This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current signal components, wherewith it is possible to gain information to detect bearing issues and classify them using statistical methods. The methodology was implemented in MATLAB using the digital Taylor–Fourier transform for three fault types (bearing ball damage, outer-race damage, and corrosion damage) at different powering conditions: power grid source at 60 Hz and adjustable speed drive applied (60 Hz, 50 Hz, 40 Hz, 30 Hz, 20 Hz, and 10 Hz) in loading and unloading conditions. Results demonstrate a classification accuracy between 93–99% for bearing ball damage, 91–99% for outer-race damage, and 94–99% for corrosion damage. [ABSTRACT FROM AUTHOR]

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Today, with the developing new technologies, in addition to the traditional usage areas of asynchronous machines, their usage areas as motors and generators are gradually expanding. In recent years, the use of induction generator in areas such as motor/generator in electric vehicles, wind turbines and microhydroelectric generation has started to widespread. This study aims to design a low-speed and high-torque outer rotor induction machine that can be used in outer rotor induction motor applications and optionally as a direct-drive generator. The design, optimization and electromagnetic analysis of a 16-pole, 50 Hz frequency, 375 rpm synchronous speed, 1 kW outer rotor induction machine were carried out and six different models were developed for the analyses in this study. One slot type and 72 slot were used in the stator designs, 59 slot and three different slot types were used in the rotor design. Copper and Aluminum materials were preferred for the squirrel cage rotor and the study was carried out with the Ansys Maxwell electromagnetic package program. In the study, machine foundation sizes, air gap and slot dimensions were optimized to obtain the highest efficiency using the Rmxprt-optimetrics module. Afterwards, electromagnetic analyzes were performed using the Finite Element Method. As a result of the analyzes made for the motor operating area of the machine, an efficiency higher than the efficiency of 8-pole inner rotor asynchronous motors with 1.1 kW power in IE2 and IE3 classes according to IEC 60034-30-1 was obtained. According to this result, it was found that an outer rotor, low speed and high torque induction machine can be produced and used as a motor. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs. [ABSTRACT FROM AUTHOR]

Based on the system approach and the principle of electrohydrodynamic analogy, a formalized mathematical model of a generalized system of asynchronous centrifugal pumping units of a multi-unit pumping station with an asynchronous electric drive of centrifugal pumps was created. This model is easy to adapt to the specific configuration of the pumping station. A feature of the model is the use of such parameters of the centrifugal pump, which are calculated directly through the geometric dimensions of its internal elements, taking into account the dependence of these parameters on the physical properties of the working fluid. Unlike existing models, this makes it possible to take into account the influence of operational or emergency changes in pump parameters and operational changes in the physical properties of the working fluid both on its pumping regimes and on the operation regimes of the electric drive directly during the simulation. The scope of use and ways of improving the developed model, as well as the direction of further research, are proposed. [ABSTRACT FROM AUTHOR]

In this paper, the Hunger Games Search (HGS) optimization algorithm is used to estimate the electrical parameters of the induction machine steady state model. Induction machine nameplate data is used as input to the proposed algorithm. The performance of the proposed method is confirmed by comparing the output characteristics obtained by estimating the motor parameters including torque, current and power factor in the steady state model of the induction machine with the values provided by the manufacturer. In addition, by evaluating and comparing the results of the proposed method with the results of previous research, it is shown that the proposed algorithm is a very effective and accurate method for the acceptable estimation of induction machine parameters.

Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.

Direct Torque Control (DTC) of Induction Motors (IMs) is popular in motor drive applications because of its robust and simple control structure. The IM winding can be controlled on both sides using dual inverter technique which more effective for Electric Vehicle (EV) with a greater number of voltage vectors. However, the battery performance of the dual inverter will deteriorate unevenly on both sides, resulting in fluctuating voltages for the EV system. This will lead to the generation of distorted stator currents and a significant droop in the stator flux, which in turn can increase the total harmonic distortion (THD) in the system. Additionally, the performance of torque may not be able to regulate effectively. This paper examines the effect of unstable voltage on voltage vector mapping performance with tilted angles and proposes new sector definitions based on voltage ratio conditions. Moreover, the proposed sector for each predefined voltage ratio is tested under three-speed conditions. The proposed technique effectiveness is validated through hardware experiments using a dSPACE 1104 controller and retuning the stator current for proper waveform. This approach improves the stator current waveform, improves stator flux droop, enhances torque regulation and minimizes the THD in the DTC system. [ABSTRACT FROM AUTHOR]

Condition monitoring of induction machines (IMs) with the aim of increasing the machine's lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions. [ABSTRACT FROM AUTHOR]

At present, the induction motor (IM), synchronous reluctance motor (SynRM), ferrite-assisted synchronous reluctance motor (ferrite-assisted SynRM) and interior permanent magnet motor (IPM) are research hotspots, but comprehensive comparative research on the four motors is still rare. This paper mainly compares the four motors from the aspects of electromagnetic performance, material cost and temperature distribution. Firstly, the volume of the four motors is ensured to be the same. The influence of the rotor design parameters of the SynRM, ferrite-assisted SynRM and IPM on the electromagnetic properties of the machine is analyzed. Secondly, based on the effects of each parameter, the overall design parameters of the four motors are determined. The electromagnetic performance, material cost and temperature of the four motors are compared and discussed. Finally, the comparison results are summarized, and the advantages of the four motors are analyzed. In different applications, the electromagnetic performance, heat dissipation and cost requirements of the four motors are different. Therefore, this paper makes a comprehensive comparison of the four motors to provide a reference for the selection of motors for different applications. [ABSTRACT FROM AUTHOR]

The work carried out in this paper proposes an improvement of the direct torque control (DTC) of induction machine by the design of modern, robust and more efficient controllers than the conventional PI controllers commonly used for speed control. A comparative study has been carried out using five controllers, i.e. PI anti-windup, first-order sliding mode control (SMC), second order sliding mode control (SOSMC), fuzzy logic controller (Fuzzy-PI) and hybrid Fuzzy second-order sliding mode controller (FSOSMC). An advanced optimization technique based on the particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers, the use of PSO for the determination of the different gains used in all controllers gives on the one hand a high accuracy performance and ensures on the other hand a reliable comparison between the different controllers in their optimal states. The simulation and analysis of each method with respect to robustness to disturbances was performed externally under various operating conditions and variations of the machine parameters. [ABSTRACT FROM AUTHOR]

The paper presents the possibility of using ERP business software for the purpose of analyzing steady states of electric drives. A centrifugal pump is used as an example of active load. After reviewing previous research and presenting the basic principles of using the ERP model, the analysis of the operation of the centrifugal pump and the processing of the results of the propulsion measurements were approached. Two flow control options are considered and both methods are compared regarding energy consumption. After that, the method of calculating energy saving using the ERP model is shown. The calculation results provide data on different costs depending on the type of flow control. Since an induction motor with a frequency converter is provided for flow control by changing the speed, the final step is rough profitability estimate of the investment in frequency converter, which was also done directly in the ERP model.