Flux-modulating machines are emerging as noteworthy machines, particularly in wind turbines. They typically have two windings on their stators, which are not directly coupled, but use a flux-modulating rotor to cross-couple the two stator windings. These brushless machines are typically medium-speed machines, and they boast reliability suitable for remote areas with low accessibility.

Two popular machines in this category are the brushless doubly fed machines (BDFMs) and wound field flux switching machines (WFFSMs). Although these machines work according to similar operating principles, they have almost contrasting descriptions. BDFMs, viewed as alternatives to doubly fed induction generators (DFIGs), are noted for their comparably lower power density and efficiency. Conversely, WFFSMs are typically touted for their high power densities, hence the suggestions of replacing PM machines.

In this paper, the performances of BDFMs and WFFSMs are compared. Select topologies that operate at the same speed are evaluated: the 4/6 BDFM and the 24/10 WFFSM. Instead of applying a direct comparison between these two topologies, optimized 250 kW designs are compared to their conventional parallel topology. BDFMs are compared with DFIGs, while WFFSMs are compared with wound field synchronous machines. The optimization processes are conducted using the non-dominated sorting genetic algorithm coupled with response surface approximations from FEA evaluated designs.

It is shown that a 4/6, 250 kW BDFM typically has about 5 % less efficiency compared to a 20-pole DFIG, while being almost two times the volume. The efficiency in WFFSMs is better due to less copper loss; however, their power factors are low. This is due to the cross-coupling nature of operations in BDFMs and WFFSMs, which leads to significantly lower flux utilization compared to their directly coupled counterparts. However, their reliability advantages cannot be dismissed.

This paper proposes an online maximum torque per ampere (MTPA)-based current control and a modified speed estimation algorithm using a full-order speed observer for field-oriented control (FOC) of a synchronous reluctance motor (SynRM) traction drive for EVs. Unlike conventional MTPA methods based on offline look-up tables, the proposed approach computes the d-axis current reference online using a current-optimizing factor. The key concept is that the current-optimizing factor adapts automatically with operating conditions to keep the excitation level optimal: when load torque increases, the flux-producing component is increased to support torque production; however, during transient events, the current-optimizing factor decreases automatically, which limits the rise in flux and current and prevents over-excitation. At low speed or light load, the current-optimizing factor maintains an optimal stator current, reducing losses and improving overall efficiency. Quantitatively, although the conventional method also maintains the operating point, the proposed optimization reduces the flux from about 1.00 pu to about 0.85 pu at nearly nominal load (0.9 pu), while improving the power factor from about 0.45 to 0.65 at low load (0.2 pu) and from about 0.75 to 0.87 at high load (0.9 pu) in both motoring and regenerative modes. Additionally, to improve the robustness of speed estimation, the full-order observer is augmented with an explicit stabilization function. The stabilization function is obtained from the dot product of stator current and flux and is used to minimize oscillations and instability during low-speed motoring and regenerative modes, including under parameter variations and transients. The proposed speed estimator is evaluated by comparing the estimated speed with the motor speed across several EV driving profiles, including motoring, constant-speed, low- and zero-speed, and regenerative modes. The results show stable operation under load–torque disturbances and accurate speed tracking across the tested profiles, demonstrating that the proposed online current-optimizing MTPA strategy and stabilization-function-enhanced full-order observer are effective for sensorless SynRM traction control.

Introduction: Asymmetrical six-phase induction machines (A6PIM) inherently excite the secondary x–y subspace, producing circulating currents that do not contribute to torque, degrade current quality, and increase losses. In recent years, virtual voltage vector (VVV) modulation has been adopted to generate variable duty-cycle switching patterns; however, under fixed switching frequency operation, it typically relies on high-dimensional lookup tables (LUTs) to map the reference voltage (Vref, θref) into duty cycles. Although effective, this approach becomes impractical for low-cost processors due to excessive memory. This work addresses that bottleneck by replacing the LUT with an AI model that reproduces the original LUT duty cycles, thereby preserving the model behavior.

Methods: A sector-based VVV formulation is adopted in the α–β plane (24 sectors, 15° each). For each sector, a fixed set of active vectors is used with a symmetric pulse mid-sequence, and duty cycles are computed to match the α–β reference while enforcing zero average x–y voltage (and thus eliminating x–y current injection). An offline dataset is generated over the operating framework in (Vref, θref), and a compact regression model is trained to approximate the duty-cycle map (Vref, θref)↦{dk}. The resulting model is exported in a lightweight format suitable for DSP implementation.

Results: The AI model reproduces the LUT duty-cycle outputs with a very small error, while reducing memory requirements from approximately 175 MB to about 39 kB. Since the duty cycles (and corresponding switching commands) are replicated, the control VVV switching pattern and switching frequency are preserved. Real-time feasibility is compatible with low-cost control hardware such as the TMS320F28379D.

Conclusions: The main contribution is a memory-efficient AI model that replaces the VVV duty-cycle LUT while reproducing the original duty cycles, preserving the control modulation structure and switching frequency, and enabling real-time implementation on low-cost controllers for A6PIM.

Current source inverters (CSIs) represent an attractive alternative to conventional voltage source inverters in high-frequency applications, owing to their reduced output voltage stress and inherent short-circuit protection. However, the mandatory insertion of overlap time between switching states, required to guarantee a continuous current path for the DC-link inductor, introduces non-linear distortions into the synthesized output currents. These distortions strongly depend on the adopted space vector modulation (SVM) strategy and can significantly degrade the power quality and efficiency of the conversion system.

This paper presents an overlap time compensation methodology applicable to a wide range of carrier-based SVM strategies for CSIs. A unified classification framework is first introduced to uniquely identify switching patterns based on the number of segments and dwell time sequences. Building on this representation, the overlap time effects are analytically characterized by linking dwell time variations to the instantaneous phase voltage relationships. The proposed compensation algorithm performs an online correction of the dwell times, restoring the ideal reference current vector independently of the selected SVM pattern.

The effectiveness of the proposed approach is validated through comprehensive simulation and experimental investigations conducted on an induction motor drive. The conversion system was implemented with a DC–DC converter pre-stage supplying the CSI. The motor test bench consisted of an induction motor mechanically coupled to a DC motor, where the former controlled the torque and the latter regulated the speed. The results demonstrate a substantial reduction in low-order harmonic distortion in both output currents and voltages, with THD reductions of up to 90% for certain modulation strategies. Furthermore, the effective amplitude modulation index is recovered, achieving the required output current level, and the overall conversion efficiency improves, with measured gains of up to two percentage points.

Multilevel inverters have recently emerged as one of the promising approaches to DC–AC conversion in several medium- and high-voltage, high-power applications. These converters can generate the output voltage by synthesizing multiple discrete levels and offer superior waveform characteristics, such as lower harmonic content, better power quality, and lower electromagnetic interference compared to conventional two-level inverter systems. Nevertheless, most of the multilevel inverter topologies proposed so far require a large number of power semiconductor switches and other associated components, thus resulting in increased circuit complexity, higher implementation cost, and elevated switching losses.

In order to overcome said limitations, this work proposes a novel topology of multilevel inverter for obtaining staircase-shaped output voltages using a minimal count of switching devices. This proposed configuration makes use of six isolated DC voltage sources along with sixteen power switches, effectively providing a forty-five-level output voltage across the load. Even with reduced switch count, this inverter provides a finely stepped output voltage with increased resolution for better output waveform quality. Another important advantage of the proposed topology is the reduction in voltage stress suffered by the semiconductor devices. The lower voltage stress improves the reliability of an inverter to a great extent and also helps to choose lower voltage-rated switches, which in turn contributes to cost and efficiency improvement. A detailed comparison is carried out on the proposed topology considering component requirement, obtainable output voltage levels, and the distribution of voltage stresses among the switches. The performance of the proposed inverter is validated by detailed simulation studies. All the simulation results show stable and reliable operations under a wide range of loading conditions, includingboth linear and nonlinear loads. These findings prove that the proposed multilevel inverter topology is quite suitable for practical implementation in medium- and high-power conversion systems.

This article presents an advanced modeling framework for inter-turn short circuits (ITSCs) in segmented permanent magnet synchronous machines (PMSMs), with a focus on highly coupled segmentation (HCS) and multisector segmentation (MSS) winding architectures. As electric traction systems increasingly demand compactness and reliability—especially in aeronautics, electric vehicles, and renewable energy—ITSCs remain a critical failure mode, often leading to severe short-circuit currents and potential system collapse. While multiphase systems enhance fault tolerance for open-circuit faults, ITSCs pose unique challenges due to their propensity to propagate within motor windings.
The study introduces a model that systematically analyzes ITSC faults in PMSMs operating under power-sharing conditions. Validated through experimental data from a dedicated laboratory test bench, the model demonstrates a strong alignment with real-world observations. A key innovation is the decoupling of intrinsic motor behavior (e.g., torque production and magnetic flux control) from the impact of power-sharing strategies, revealing how short-circuit currents are influenced by both resistive and inductive terms highly dependent on the segmentation technology.
Comparative analysis highlights that MSS configurations exhibit greater sensitivity to power-sharing variations than HCS. This sensitivity stems from disparities in magnetic coupling: minimal in HCS but pronounced in MSS, where differential inductances significantly affect short-circuit currents. The findings underscore that while HCS maintains stability under power-sharing adjustments, MSS requires careful control to mitigate fault propagation risks.
The model’s predictive capabilities—spanning speed, short-circuit resistance, and differential current variations—provide actionable insights for designing fault-tolerant, power-sharing-capable drives. This work advances the understanding of ITSC dynamics in segmented PMSMs, offering a robust foundation for optimizing motor resilience in high-reliability applications.

The reliability of Direct Current (DC) motors is critical to industrial productivity, yet mechanical components such as commutators and brushes are highly susceptible to wear and failure. This paper presents a rigorous comparative analysis of machine learning-based Fault Detection and Diagnosis (FDD) frameworks for Permanent Magnet DC (PMDC) motors. Specifically, we evaluate and compare the diagnostic performance of K-Nearest Neighbors (KNNs), Artificial Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks in classifying three operational states: Normal, Brush-Wear, and Commutator-Fault. Utilizing an open-source industrial dataset, each model was optimized to distinguish between subtle fault signatures that often lead to unplanned downtime. Our experimental results demonstrate a clear performance hierarchy: the baseline KNN model achieved an accuracy of 93.3% but was vulnerable to overlapping feature spaces, whereas the ANN improved accuracy to 94.7% by capturing non-linear relationships. The proposed LSTM architecture significantly outperformed both models, achieving a superior validation accuracy of 97.4% and near-perfect precision for normal and brush-wear conditions. This superior performance is attributed to the LSTM’s specialized gating mechanisms, which effectively capture long-term temporal dependencies within motor current and vibration signals. The study concludes that temporal deep learning models offer the most robust solution for automated predictive maintenance in complex industrial environments.

Wind energy is regarded as a critical point within the global energy market, especially for those countries that depend on this source to produce electrical energy. The efficiency of wind energy is, to a large extent, dependent on the efficiency of the transmission system parts, such as the gear. In wind energy, the gear is involved in the transmission of mechanical energy from the rotor to the generator. The intention of this study is to assess and compare the performance of spur and chevron gears in wind turbines, to fill the gap regarding the difference between theoretical and experimental efficiency related to high-speed winds. The study was based on comparisons of the performance between the theoretical and experimental results, and the results reveal that the difference between the theoretical and experimental performances for the spur gears was very low (less than 0.1%), indicating the accuracy of the theoretical models and their applicability to high-speed performance. Furthermore, the results indicated a significant discrepancy (up to 0.46%) for the chevron gears, related to certain elements not considered in the theory, such as lateral friction. In conclusion, this study demonstrated that spur gears are more reliable in predicting efficiency while calling for the development of more comprehensive theoretical models for chevron gears.

Near-infrared (NIR) imaging systems have demonstrated significant potential for enhancing subcutaneous visualization applications; however, existing solutions are typically based on proprietary architectures with limited accessibility for experimental and engineering development. This work presents the mechanical and mechatronic design of a low-cost NIR vein visualization platform focused on optoelectronic integration, structural stability, and real-time image acquisition performance. The proposed system combines a controlled NIR illumination module, an infrared-sensitive imaging unit, and a compact processing architecture designed to operate under constrained computational resources while maintaining consistent visualization conditions. The mechanical structure was developed to ensure fixed spatial alignment between illumination and sensing components, minimizing optical noise and shadow artifacts through controlled geometry and working distance optimization. Image processing routines based on grayscale conversion and contrast enhancement were implemented to improve signal differentiation between vascular and surrounding tissue structures while preserving real-time operation capability. The modular architecture enables rapid prototyping, component scalability, and reproducibility using commercially available hardware elements. Experimental functional evaluation demonstrated stable visualization of superficial vascular patterns under controlled conditions, confirming adequate optical response and system repeatability for continuous operation scenarios. Compared with commercially available systems, the proposed platform significantly reduces implementation cost while preserving essential functional performance required for optoelectronic sensing applications. The presented design highlights the potential of low-cost mechatronic integration for developing scalable optical sensing platforms, providing a foundation for future improvements involving automated calibration, advanced image processing, and adaptive control strategies in electromechatronic systems.

Maritime piracy continues to pose a persistent threat to global shipping routes, offshore assets, and international trade, particularly in regions characterized by limited surveillance infrastructure and political instability. Conventional anti-piracy strategies primarily depend on manual watchkeeping, crew training, and reactive response procedures, which may be insufficient in rapidly evolving threat scenarios. This paper investigates the role of advanced mechatronic systems in enhancing proactive protection through automated detection of piracy-related risks. This study presents a comprehensive analysis of current technologies that combine multisensory data acquisition, including radar, electro-optical sensors, acoustic monitoring, and satellite-based positioning, with intelligent data processing techniques. Special attention is given to the integration of machine learning algorithms and sensor fusion methods that enable real-time identification of suspicious vessel behavior and anomalous movement patterns.

A conceptual architecture of an integrated mechatronic threat detection system is proposed, focusing on modular design, system reliability, and compatibility with existing onboard machinery and control infrastructures. This paper evaluates technical challenges associated with operation in harsh marine environments, such as signal interference, environmental noise, system robustness, and cybersecurity vulnerabilities. Strategies for minimizing false alarms and ensuring dependable performance under varying sea and weather conditions are discussed. Additionally, the potential use of unmanned aerial and surface vehicles as extensions of onboard detection systems is examined as a means of expanding situational awareness.

Our findings suggest that automated mechatronic detection technologies can significantly improve early warning capabilities, support faster decision-making, and reduce the cognitive workload of ship crews. By bridging principles of mechatronics, automation, and maritime security, this research highlights the importance of interdisciplinary engineering solutions in addressing contemporary safety challenges. The proposed framework contributes to the development of smarter and more resilient maritime protection systems capable of adapting to evolving piracy threats.