Search Results (1,321)

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

Power electronic devices for AC/DC and AC/AC conversion are, nowadays, widely distributed in electrified transportation and industrial applications, which can determine significant deviation in supply voltage waveform from the AC sinusoidal and promote insulation extrinsic aging mechanisms as partial discharges (PDs). PDs are one of the most harmful processes as they are able to cause accelerated extrinsic aging of electrical insulation systems and are the cause of premature failure in electrical asset components. PD phenomenology under pulse width modulated (PWM) voltage waveforms has been dealt with in recent years, also through some IEC/IEEE standards, but less work has been performed on PD harmfulness under AC distorted waveforms containing voltage harmonics and notches. On the other hand, these voltage waveforms can often be present in electrical assets containing conventional loads and power electronics loads/drives, such as for ships or industrial installations. The purpose of this paper is to provide a contribution to this lack of knowledge, focusing on PD sensing and phenomenology. It has been shown that PD patterns can change considerably with respect to those known under sinusoidal AC when harmonic voltages and/or notches are present in the supply waveform. This can impact PD typology identification, which is based on features related to PD pattern-based physics. The adaptation of identification AI algorithms used for AC sinusoidal voltage as well as distorted AC waveforms is discussed in this paper, showing that effective identification of the type of defects generating PD, and thus of their harmfulness, can still be achieved. Full article

To accurately evaluate the health condition of the cables of a cross-sea cable-stayed bridge under typhoon effects and to improve the efficiency of damage identification, an accurate bridge damage identification method combining convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. A numerical model of the cable-stayed bridge was first established in ANSYS. Based on the monitoring data of Super Typhoon Mujigae, a three-dimensional fluctuating wind field was generated by harmonic synthesis. Through transient analysis, the static and dynamic responses of the cable-stayed bridge under typhoon loads were analyzed, and the critical cable locations most susceptible to damage were identified. Subsequently, the acceleration signals of the structural damage states under typhoon were extracted, and the damage-sensitive features were obtained through the Hilbert transform. Finally, an intelligent damage identification model for cable-stayed bridges was established by combining CNN and BiLSTM, and the identification results were compared with those obtained using CNN and BiLSTM individually. The results indicate that the neural network model combining CNN and BiLSTM performs significantly better than either CNN or BiLSTM alone in predicting both the location and degree of damage. Compared with the standalone CNN and BiLSTM models, the proposed hybrid CNN–BiLSTM network improves the accuracy of damage-location identification by 1.6% and 2.42%, respectively, and achieves an overall damage-degree identification accuracy exceeding 98%. The findings of this study provide theoretical and practical support for the intelligent operation and maintenance of cable-stayed bridges in coastal regions. The proposed approach is expected to serve as a valuable reference for evaluating large-span bridge structures under extreme wind conditions. Full article

This paper focuses on the vibration analysis of a prototype helicopter rotor test stand, with particular attention to the dynamic response of its electric propulsion system. The stand is driven by an induction motor and equipped with composite rotor blades of various geometries, including blades with shape memory alloy (SMA)-based torsion actuators for angle of attack (AoA) adjustment. These variable geometries significantly influence the system’s dynamic behavior, where resonance phenomena may pose risks to structural integrity. The objective was to investigate how selected operational parameters specifically motor speed and AoA affect the vibration response of the propulsion system. Structural vibrations were measured using a tri-axial piezoelectric accelerometer system integrated with calibrated signal conditioning and high-resolution data acquisition modules. This setup enabled precise, time-synchronized recording of dynamic responses along all three axes. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods were applied to identify dominant frequency components, including those associated with rotor harmonics and SMA activation. The highest vibration amplitudes were observed at an AoA of 16°, but all results remained within the vibration limits defined by MIL-STD-810H for rotorcraft drive systems. The study confirms the importance of sensor-based diagnostics in evaluating electromechanical propulsion systems operating under dynamic loading conditions. Full article

The rapid advancement of more electric aircraft technology has led to the widespread integration of non-linear loads into aircraft power supply systems. Passivity-based control (PBC) is a well-established method for enhancing system stability. However, existing research mainly focuses on current-controlled converters with control strategies confined to the fundamental component, while studies on passivity control for voltage-controlled converters incorporating harmonic mitigation remain limited. To enhance the stability of the standalone converters in aircraft power systems, this paper first proposes a method that transforms the converter output impedance into a product of two sub-impedances, thereby revealing the compensation mechanism of the output current feedforward active damping on the converter output impedance. Based on this insight, a second-order generalized integrator based active damping strategy is introduced to achieve sub-impedance phase compensation. Furthermore, to mitigate the adverse effect on converter passivity introduced by resonant controllers, a phase lead angle design strategy is proposed to ensure converter passivity from 0 Hz to the Nyquist frequency without compromising harmonic mitigation capability. Experimental results on a 5 kW three-phase converter validate the effectiveness of the proposed method. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

Periodontitis is a biofilm-driven inflammatory disease in which conventional indices (probing depth, clinical attachment level, and radiographs) quantify tissue destruction without capturing the biology of infection. In this review, we synthesized microbiological diagnostics, from chairside tools to omics. We outline sampling strategies and emphasize the quantitative monitoring of bacterial load. Enzymatic assays (e.g., N-benzoyl-DL-arginine-2-naphthylamide hydrolysis assay test) measure functional activity at the point of care. Immunological methods include rapid immunochromatography for Porphyromonas gingivalis and enzyme-linked immunosorbent assay for the high-throughput measurement of bacterial antigens. Molecular platforms encompass quantitative polymerase chain reaction (qPCR) (TaqMan, SYBR, multiplex panels; propidium monoazide quantitative-qPCR for viable cells), checkerboard DNA–DNA hybridization for semi-quantitative community profiling, loop-mediated isothermal amplification (LAMP)/molecular beacon-LAMP for portable isothermal detection, and microarrays. Complementary modalities such as fluorescent in situ hybridization, next-generation sequencing, and Fourier transform infrared spectroscopy provide spatial, ecological, and biochemical resolutions. We discuss the limitations of current approaches, including sampling bias, presence–activity discordance, semi-quantitation, method biases, limited strain/function resolution, low-biomass artifacts, and lack of validated cutoffs. To address these challenges, we propose a pragmatic hybrid strategy: site-specific quantitative panels combined with activity and host-response markers interpreted alongside clinical metrics under standardized quality assurance/quality control. Priorities include outcome-linked thresholds, strain-aware/functional panels, robust point-of-care chemistry, and harmonized protocols to enable personalized periodontal care. Full article

A multi-port conversion system that connects photovoltaic (PV) arrays, battery energy storage (BES), and an electric vehicle (EV) to a single-phase grid offers a flexible solution for smart homes. By integrating Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies, the system supports bidirectional energy flow, optimizing usage, improving grid stability, and supplying backup power. The proposed four-port converter consists of an interleaved bidirectional DC-DC converter for high-voltage BES, a bidirectional buck–boost DC-DC converter for EV charging and discharging, a DC-DC boost converter with MPPT for PV, and a grid-tied inverter. Its non-isolated structure ensures high efficiency, compact design, and fewer switches, making it suitable for residential applications. A state-of-charge (SoC)-based power management strategy coordinates operation among PV, BES, and EV in both on-grid and off-grid modes. It reduces reliance on EV energy when supporting V2G and V2H, while SoC balancing between BES and EV extends lifetime and lowers current stress. A 7.5 kVA system was simulated in MATLAB/Simulink to validate feasibility. Two scenarios were studied: PV, BES, and EV with V2G supporting the grid and PV, BES, and EV with V2H providing backup power in off-grid mode. Tests under PV fluctuations and load variations confirmed the effectiveness of the proposed design. The system exhibited a fast transient response of 0.05 s during grid-support operation and maintained stable voltage and frequency in off-grid mode despite PV and load fluctuations. Its protection scheme disconnected overloads within 0.01 s, while harmonic distortions in both cases remained modest and complied with EN50610 standards. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

In this study, the nonlinear forced vibration response of fiber-reinforced laminated composite beams coated with functionally graded materials (FGMs) is investigated under the combined action of aero-thermoelastic loads and a concentrated harmonic excitation. The mathematical formulation is established using the Euler–Bernoulli beam theory, where von Kármán geometric nonlinearities are taken into account, along with the modified third-order piston theory to represent aerodynamic effects. By neglecting axial inertia, the resulting set of nonlinear governing equations is simplified into a single equation. This equation is discretized through the Galerkin procedure, yielding a nonlinear ordinary differential equation. An analytical solution is, then, obtained by applying the method of multiple time scales (MTS). Furthermore, a comprehensive parametric analysis is carried out to evaluate how factors such as the power-law index, stacking sequence, temperature field, load amplitude and position, free-stream velocity, and Mach number influence both the lateral dynamic deflection and the frequency response characteristics (FRCs) of the beams, offering useful guidelines for structural design optimization. Full article

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article

This research explores a phase-shift-based discontinuous PWM method used for 24 V battery-powered onboard micro inverters, which are critical for thermally limited applications like micromachines, where efficient heat dissipation and compact size are paramount. Discontinuous pulse width modulation (DPWM) reduces switching losses by clamping the phase voltage to the DC bus in order to improve inverter efficiency. Due to the change in power factor at different operating points from motors or the inductor load, the use of only one DPWM method cannot achieve the optimal efficiency of a three-phase voltage source inverter (3ph-VSI). This paper proposes a generalized DPWM method with a continuously adjustable phase shift angle, which extends the six traditional DPWM methods to any type. According to different power factors, the proposed DPWM method is divided into five power factor angle intervals, namely [−90°, −60°], [−60°, −30°], [−30°, 30°], [30°, 60°], and [60°, 90°], and automatically adjusts the phase shift angle to the optimal-efficiency DPWM mode. The power factor is calculated by means of the Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) method. The switching losses and harmonic characteristics of the proposed DPWM are analyzed, and finally, a 24 V onboard 3ph-VSI experimental platform is built. The experimental results show that the efficiency of DPWM methods can be improved by 3–6% and the switching loss can be reduced by 40–50% under different power factors. At the same time, the dynamic performance of the proposed algorithm with a transition state is verified. This method is particularly suitable for miniaturized inverters where efficiency and thermal management are critical. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes. Full article