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The metallic components of an electric machine exert a notable impact on the electromagnetic transmission between an external recipient and an internal shaft sensor. The RTMS (rotor temperature monitoring system) is aimed at boosting power transfer in the machine, regardless of the considerable impact of its components. This research assesses the influence of the components of an electric machine on shaft sensor transmission and specifically addresses the packaging needs of the receiver antenna. The study includes a comparative analysis of two antennas. HFSS (high frequency structure simulator) simulation was utilized to identify the antenna with a superior propagation factor. The principal contribution of this paper is the assessment of how the electrical machine body affects the transmission and propagation of rotor sensor signals, establishing a connection between these effects and the packaging criteria for the receiver antenna. Moreover, the paper presents an antenna design that capitalizes on the electrical machine body to enhance the power transmission effectiveness. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, provides valuable insights into the propagation characteristics, and contributes to a comprehensive understanding of the electromagnetic dynamics within the system. [ABSTRACT FROM AUTHOR]

In PMSM motor drive systems, control accuracy and resistance have always been the most important challenges for researchers. This paper addresses these two challenges and presents a control structure that makes the system performance robust against model uncertainties of system disturbances as well as sudden load changes. In other words, in this paper, an improved MPC controller has been presented, in which model uncertainties and possible disturbances on the system are considered during design. In addition, in order to apply the physical limitations of the electric motor during the design of this improved controller, the input limitations of the system have also been considered. Therefore, the desired MPC controller is a resistant and constrained MPC, it is resistant to uncertainties and disturbances. In addition, in order to further strengthen the control loop against sudden load changes, a sliding estimator has also been designed to estimate the load torque. This estimator itself is also resistant to the uncertainties of the model, which makes the entire control structure have a very robust performance. The performance of the method presented in this article has been compared with a conventional sliding method during a series of practical tests. The results showed that the proposed method has a higher accuracy of 3 rpm and a shorter convergence time of 2 seconds.

This paper discusses the construction and operating principle of an axial flux electric machine with hybrid excitation. Based on computer simulations using the Finite Element Method, an analysis was conducted with changes in the geometry of the magnetic circuit, which involves the rotation of the rotor disks relative to each other on the operating parameters of the machine. Both the generator state of operation, in the meaning of analyzing the induced voltage (adjustment at −11% ÷ +64%) and the cogging torque, and the motor state of operation, in the meaning of analyzing the ripple of the electromagnetic torque (possible reduction by almost 30%), were examined. The article concludes with observations on how the change in the angle of the rotor disks affects the efficiency of the disk machine with axial flux and hybrid excitation. [ABSTRACT FROM AUTHOR]

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data's underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques' applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor's variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers. [ABSTRACT FROM AUTHOR]

Finding reliable and robust criteria for the detection of broken bars in induction motors is key for the maintenance of industrial engines, and some of the most efficient methods analyze the stator start-up current. Due to the transitory characteristics and short duration of the signal, suitable time-frequency mathematical tools are very useful for this purpose. We propose here algorithms based on the discrete wavelet and wavelet packet transform, combined with other tools in signal processing, to offer an accurate quantitative method for failure detection due to broken bars in induction motors. A good selection of the wavelet family is important for a good performance of the indicator, and the discrete approximation of the Meyer wavelet, 'dmeyer', consistently demonstrates the most favorable results. Our findings highlight the effectiveness of both the wavelet and wavelet packet transforms in accurately detecting broken bars in induction motors. This fact allows optimal monitoring strategies in industrial applications. [ABSTRACT FROM AUTHOR]

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The metallic components of an electric machine exert a notable impact on the electromagnetic transmission between an external recipient and an internal shaft sensor. The RTMS (rotor temperature monitoring system) is aimed at boosting power transfer in the machine, regardless of the considerable impact of its components. This research assesses the influence of the components of an electric machine on shaft sensor transmission and specifically addresses the packaging needs of the receiver antenna. The study includes a comparative analysis of two antennas. HFSS (high frequency structure simulator) simulation was utilized to identify the antenna with a superior propagation factor. The principal contribution of this paper is the assessment of how the electrical machine body affects the transmission and propagation of rotor sensor signals, establishing a connection between these effects and the packaging criteria for the receiver antenna. Moreover, the paper presents an antenna design that capitalizes on the electrical machine body to enhance the power transmission effectiveness. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, provides valuable insights into the propagation characteristics, and contributes to a comprehensive understanding of the electromagnetic dynamics within the system.

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques’ applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor’s variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers.

The article proposes an approach to determining the uneven air gap of a synchronous machine with prismatic magnets on the rotor, in which the distribution of the normal component of magnetic induction along the inner circumference of the stator core has a shape close to sinusoidal. The calculation of the magnetic field of an electric machine model with the specified geometric dimensions and properties of the element materials is performed in the FEMM program. To solve the optimization problem, the Hook-Jeeves method is used, restrictions are taken into account by the method of penalty functions. As a result of modeling the magnetic field of an electric machine with a calculated optimal uneven air gap, a distribution curve of the normal component of magnetic induction along the inner circumference of the stator is obtained, the average deviation of which from the sinusoidal curve is 4,8 %.

Finding reliable and robust criteria for the detection of broken bars in induction motors is key for the maintenance of industrial engines, and some of the most efficient methods analyze the stator start-up current. Due to the transitory characteristics and short duration of the signal, suitable time-frequency mathematical tools are very useful for this purpose. We propose here algorithms based on the discrete wavelet and wavelet packet transform, combined with other tools in signal processing, to offer an accurate quantitative method for failure detection due to broken bars in induction motors. A good selection of the wavelet family is important for a good performance of the indicator, and the discrete approximation of the Meyer wavelet, ‘dmeyer’, consistently demonstrates the most favorable results. Our findings highlight the effectiveness of both the wavelet and wavelet packet transforms in accurately detecting broken bars in induction motors. This fact allows optimal monitoring strategies in industrial applications.

With the increasing demand for smaller, lighter, and more affordable electromagnetic devices, there is a growing trend toward developing high-power-density transformers and electrical machines. While increasing the operating frequency is a straightforward approach to achieving high power density, it can lead to significant power loss within a limited volume, resulting in excessive temperature rise and device degradation. Therefore, it is crucial to design high-power-density electromagnetic devices that exhibit low power loss and efficient thermal dissipation to address these challenges. Advanced techniques, such as the utilization of novel and advanced electromagnetic materials, hold great promise for overcoming these issues. Specifically, nanocrystalline and amorphous magnetic materials have emerged as highly effective solutions for reducing power loss and increasing efficiency in electromagnetic devices. This paper aims to provide an overview of the application of nanocrystalline and amorphous magnetic materials in transformers and electrical machines, along with key technologies and the major challenges involved. [ABSTRACT FROM AUTHOR]

Direct drive wind turbine generators with superconducting excitation winding are studied with focus on the electromagnetic damper design. A semi‐analytical eddy current model is built for parametric design studies based on a vector potential approach. The considered generators employ open stator slots and exhibit strong saturation effects, so that an analytical expression for the source terms is not adequate. Whilst the field equations are solved analytically, an equivalent current loading, accounting for slotting effects and saturation, is obtained by magnetostatic models applying the 2D finite element method (FEM). The proposed framework for the extraction of the current loading is universal and may also be applied to other machine types (e.g., permanent magnet synchronous machines, [PMSM]). In relation to transient FEM models, a considerable time‐saving can be achieved, which is particularly beneficial in case of large models, for example, in case of fractional slot windings or intermittent feeding schemes. The eddy current loss in warm and cold rotor parts in a direct drive generator in the 7 MW power class are computed with the semi‐analytical and transient 2D FEM models under variation of the damper geometry and the stator winding configuration. Minimum damper dimensions as well as constraints regarding applicable stator windings are derived. [ABSTRACT FROM AUTHOR]

This research paper explores the use of chatGPT to facilitate the development of educational experiences in the Roblox metaverse, specifically focusing on the electrical machine course. The primary objectives of this study are to demonstrate how chatGPT can streamline the creation of immersive and interactive learning environments within Roblox and to evaluate the efficacy of these experiences in engaging students and enhancing their understanding of electrical machines. Our approach leverages chatGPT's capabilities to optimize client-side scripts for server-side implementation efficiently, create well-structured dictionaries for describing game activities, stages, and points, and simplify the implementation of various effects and interactions. Upon completing our Robloxbased educational experience, the resulting code will be available under a Creative Commons license, allowing other educators and developers to build upon and customize our work for their needs. We tested this project with a group of 22 college-level students studying electrical machines, whose feedback has been instrumental in understanding the effectiveness of our methods and identifying areas for improvement. The results of this study suggest that integrating chatGPT into the development process of Roblox-based electrical machine-learning experiences can lead to increased student engagement and understanding. Further research is recommended to explore the broader implications of using chatGPT and other AI-powered tools in developing metaverse-based educational content across various subjects and learning environments. By doing so, we can continue to push the boundaries of what is possible in virtual education and provide more engaging and effective learning experiences for students worldwide. [ABSTRACT FROM AUTHOR]

This paper proposes an estimation model of hysteresis loss based on the mapping of the hysteresis loop conducted through tiny material samples with the aid of the Superconducting Quantum Interference Device (SQUID). This approach allows precise measurement of hysteresis loops. The aim of this paper is to develop a precise estimation model for hysteresis loss, one component of core loss, so as to improve the overall estimation accuracy of the entire core loss under complex excitations. First, with the help of the SQUID system and measurements conducted on tiny samples, the proposed model manages to minimize the impact of eddy current throughout the measurement and the modeling of hysteresis loss. This contrasts with the use of the conventional Steinmetz model and its modifications. Moreover, compared with methods including the modified Steinmetz equation (MSE) and the generalized Steinmetz equation (GSE) which merely focuses on the overall iron loss under simple non-sinusoidal excitations, the model presented here enables the estimation of hysteresis loss under complex excitations where multiple peaks and DC-bias take place. This approach will be very useful in situations such as those requiring pulse-width modulation (PWM) excited converters which are quite common on occasions including wind power systems. The estimation error of this model on the actual coil is within 15% under given excitations.

An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the current technologies and future requirements of electrical machine in automotive. First, a comparative of ten different electrical machines based on the market available electrical vehicles and hybrid electrical vehicles is made. The aim of the comparison is to evaluate the different electrical machine topologies, structure, operation conditions, and performance. The future design requirements of electrical machine in automotive are presented and several state-of-art electrical machine design techniques have been explored and studied by employing them in the compared ten machines. The conclusion of this work shows that permanent magnet topologies have the highest performance among the different electrical machine topologies and have the potential to meet the future requirements. In addition, aggressive cooling methods, i.e., spray, dripping, semi- or fully flooded, are needed to allow electrical machines to operate with the required power density target. Finally, the design to recycle concept shows the potential to be implemented in future electrical machines with little or no sacrifice of the performance. [ABSTRACT FROM AUTHOR]