Search Results (569)

The increasing frequency of climate- and cyber-induced blackouts in modern distribution networks calls for restoration strategies that are both resilient and control-aware. Traditional black-start schemes, based on predefined energization sequences from synchronous machines, are inadequate for inverter-dominated grids characterized by high penetration of distributed energy resources and limited system inertia. This paper proposes a novel multi-layered black-start planning framework that explicitly incorporates the dynamic capabilities and operational constraints of grid-forming energy storage systems (GFESs). The approach formulates a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), jointly minimizing total restoration time, voltage–frequency deviations, and maximizing early-stage load recovery. A graph-theoretic partitioning module identifies restoration subgrids based on topological cohesion, critical load density, and GFES proximity, enabling localized energization and autonomous island formation. Restoration path planning is embedded as a mixed-integer constraint layer, enforcing synchronization stability, surge current thresholds, voltage drop limits, and dispatch-dependent GFES constraints such as SoC evolution and droop-based frequency support. The model is evaluated on a modified IEEE 123-bus system with five distributed GFES units under multiple blackout scenarios. Simulation results show that the proposed method achieves up to 31% faster restoration and 46% higher voltage compliance compared to MILP and heuristic baselines, while maintaining strict adherence to dynamic safety constraints. The framework yields a diverse Pareto frontier of feasible restoration strategies and provides actionable insights into the coordination of distributed grid-forming resources for decentralized black-start planning. These results demonstrate that control-aware, partition-driven optimization is essential for scalable, safe, and fast restoration in the next generation of resilient power systems. Full article

In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and discusses the critical aspects of the CHIL implementation process, emphasizing the relevance of the control delays that arise from sampling, computation, and pulse width modulation (PWM), which also adversely affect system stability, accuracy, and performance. Time and frequency domains are used to validate the modeling of the system, either to represent large-signal or small-signal models. This work shows multiple representations of the system under study: the fundamental frequency model, the switched model, and the switched model controlled by the DSP, are used to validate the nonlinear model, whereas the impedance-based modeling is followed to validate the linear representation. The results demonstrate a strong correlation among the models, confirming that the delay effects are accurately captured in the different simulation approaches. This comparison provides valuable insights into configuration practices that improve the fidelity of CHIL-based validation and supports impedance-based stability analysis in power electronic systems. The findings are particularly relevant for wideband modeling and real-time studies in electromagnetic transient analysis. Full article

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients. Full article

Robust speed estimation in induction motors (IM) is essential for control systems (especially in sensorless drive applications) and condition monitoring. Traditional model-based techniques for inverter-fed IM provide a high accuracy but rely heavily on precise motor parameter identification, requiring multiple sensors to monitor the necessary variables. In contrast, model-independent methods that use rotor slot harmonics (RSH) in the stator current spectrum offer a better adaptability to various motor types and conditions. However, many of these techniques are dependent on full-band processing, which reduces noise immunity and increases computational cost. This paper introduces a novel subband signal processing approach for rotor speed estimation focused on RSH tracking under both steady and non-steady states. By limiting spectral analysis to relevant content, the method significantly reduces computational demand. The technique employs an advanced time-frequency analysis for high-resolution frequency identification, even in noisy settings. Simulations and experiments show that the proposed approach outperforms conventional RSH-based estimators, offering a robust and cost-effective solution for integrated speed monitoring in practical applications. Full article

Three-level non-inverting buck–boost converters are promising for electric vehicle charging stations due to their wide voltage regulation capability and bidirectional power flow. However, the number of three-level operating states is four times that of two-level operating states, and the lack of a unified switching state selection mechanism leads to serious challenges in its application. To address these issues, a finite control set model predictive control (FCS-MPC) strategy is proposed, which can determine the optimal set and select the best switching state from the excessive number of states. Not only does the proposed method achieve fast regulation over a wide voltage range, but it also maintains the input- and output-side capacitor voltage balance simultaneously. A further key advantage is that the number of switching actions in adjacent cycles is minimized. Finally, a hardware-in-the-loop experimental platform is built, and the proposed control method can realize smooth transitions between multiple operation modes without the need for detecting modes. In addition, the state polling range and the number of switching actions are superior to conventional predictive control, which provides an effective solution for high-performance multilevel converter control in energy systems. Full article

With the widespread deployment of mobile imaging sensors and smart devices, the risk of image privacy leakage is increasing daily. Protecting sensitive information in captured images has become increasingly critical. Existing image privacy protection measures usually rely on manual blurring and occlusion, which are inefficient, prone to omitting privacy information, and have an irreversible impact on the usability and quality of images. To address these challenges, this paper proposes TSLEPS (Two-Stage Localization and Erasure method for Privacy protection in Sensor-captured images). TSLEPS adopts a two-stage framework comprising a privacy target detection sub-model and a privacy text erasure sub-model. This method can accurately locate and erase the private text areas in images while maintaining the visual integrity of the images. In the stage of detecting privacy targets, an inverted residual attention mechanism is designed and combined with a generalized efficient aggregation layer network, significantly improving privacy target detection accuracy. In the stage of privacy text erasure, a texture-enhanced feature attention mechanism is proposed with an adversarial generative network for the erasure task to achieve efficient erasure of privacy texts. Moreover, we introduce the half-instance normalization block to reduce the computational load and inference time so that it can be deployed on resource-constrained mobile devices. Extensive experiments on multiple public real-world privacy datasets demonstrate outstanding performance, with privacy target detection achieving 97.5% accuracy and 96.4% recall, while privacy text erasure reaches 38.2140 dB PSNR and 0.9607 SSIM. TSLEPS not only effectively solves the privacy protection challenges in sensor-captured images through its two-stage framework, but also achieves breakthrough improvements in detection accuracy, erasure quality, and computational efficiency for resource-constrained devices. Full article

Partial shading condition (PSC) for photovoltaic (PV) arrays complicates the operation of PV systems at peak power due to the existence of multiple peak points on the power–voltage (P–V) characteristic curve. Identifying the global peak among multiple peaks presents challenges, as the system may become trapped at a local peak, potentially resulting in significant power loss. Power generation is reduced, and hot-spot issues might arise, which can cause shaded modules to fail, under the partly shaded case. In this paper, instead of focusing on local peaks, several effective, precise, and dependable maximum power point tracker (MPPT) systems monitor the global peak using a fuzzy logic controller. The suggested method can monitor the total of all PV array peaks using an interleaved boost converter DC/DC (IBC), not only the global peaks. A DC/DC class boost converter (CBC), the current gold standard for traditional control methods, is pitted against the suggested converter. Four PSC-PV systems employ three-phase inverters to connect their converters to the power grid. Full article

Certain parasites manipulate host behavior following infection to enhance their own dispersal and transmission. Lepidopteran larvae infected with baculoviruses exhibit increased locomotion, ascending to the apex of their host plant where they ultimately die in a characteristic inverted, liquefied posture suspended by their prolegs—a phenomenon termed “tree-top disease”. Although numerous studies have investigated the underlying causes of this behavior, the precise mechanism governing tree-top disease formation remains unresolved. In this study, Lymantria dispar larvae were infected with Metarhizium anisopliae and Lymantria dispar multiple nucleopolyhedrovirus (LdMNPV). We compared symptom profiles across infection modes and assessed virulence, demonstrating that M. anisopliae infection alters the hyperactive state induced by LdMNPV in larvae exhibiting tree-top disease. Specifically, M. anisopliae promoted tree-top disease behavior during early infection stages but suppressed it during later stages. Furthermore, the symptomatology of larvae co-infected with both pathogens differed significantly from that observed in larvae infected with either M. anisopliae or LdMNPV alone. Co-infected larvae also exhibited accelerated mortality compared to those infected with a single pathogen. The above findings indicate that L. dispar larvae, when co-infected with LdMNPV and M. anisopliae may change behavioral responses that could further modulate the pathogenesis of LdMNPV-induced tree-top disease. Furthermore, a synergistic interaction between M. anisopliae and LdMNPV was observed in the biocontrol of L. dispar. Full article

Although through tied-arch bridges exhibit strong structural robustness, collapse incidents triggered by the progressive failure of hangers still occasionally occur. Given that such bridges are unlikely to collapse due to the damage of a single or multiple hangers under the serviceability limit state, this study focuses on the failure safety limit state. Using the Nanfang’ao Bridge with inclined hangers and the Liujiang Bridge with vertical hangers as case studies, this paper investigates the dynamic response and failure modes of the residual structures when single or multiple hangers fail and initiate progressive collapse of all hangers. The results demonstrate that the configuration of hangers significantly influences the distribution of structural importance coefficients and the load transmission paths. Under identical failure scenarios, the Nanfang’ao Bridge with inclined hangers remains stable after the failure of four hangers without experiencing progressive collapse, whereas the Liujiang Bridge with vertical hangers undergoes progressive failure following the loss of only three hangers, which indicates that inclined hanger configurations offer superior resistance to progressive collapse. Based on the aforementioned analysis, the LS-DYNA Simple–Johnson–Cook damage model was employed to simulate the collapse process. The extent of damage and ultimate failure modes of the two bridges differ significantly. In the case of the Nanfang’ao Bridge, following the progressive failure of the hangers, the bridge deck system lost lateral support, leading to excessive downward deflection. The deck subsequently fractured at the mid-span (1/2 position) and collapsed in an inverted “V” shape. This failure then propagated to the tie bar, inducing outward compression at the arch feet and tensile stress in the arch ribs. Stress concentration at the connection between the arch columns and arch rings ultimately triggered global collapse. For the Liujiang Bridge, failure initiated with localized concrete cracking, which propagated to reinforcing bar yielding, resulting in localized damage within the bridge deck system. These observations indicate that progressive stay cable failure serves as the common initial triggering mechanism for both bridges. However, differences in the structural configuration of the bridge deck systems, the geometry of the arch ribs, and the constraint effects of the tie bar result in distinct failure progression patterns and ultimate collapse behaviors between the two structures. Thereby, design recommendations are proposed for through tied-arch bridges, from the aspects of the hanger, arch rib, bridge deck system, and tie bar, to enhance the resistance to progressive collapse. Full article

This paper presents a microcontroller-based technique for accurately measuring resistive sensors over a wide dynamic range using an adaptive constant current source. Unlike conventional voltage dividers or fixed-current methods—often limited by reduced resolution and saturation when sensor resistance varies across several decades—the proposed system dynamically adjusts the excitation current to maintain optimal Analog-to-Digital Converter (ADC) input conditions. The measurement circuit employs a fixed reference resistor and an inverting amplifier configuration, where the excitation current is generated by one or more pulse-width modulated (PWM) signals filtered through low-pass RC networks. A microcontroller selects the appropriate PWM channel to ensure that the output voltage remains within the ADC’s linear range. To support multiple sensors, an analog switch enables sequential measurements using the same dual-PWM current source. The full experimental implementation uses four op-amps to support modularity, buffering, and dual-range operation. Experimental results show accurate measurement of resistances from 1 kΩ to 100 kΩ, with maximum relative errors of 0.15% in the 1–10 kΩ range and 0.33% in the 10–100 kΩ range. The method provides a low-cost, scalable, and digitally controlled solution suitable for embedded resistive sensing applications without the need for high-resolution ADCs or programmable gain amplifiers. Full article

Refractory full-thickness macular holes (rFTMHs) present a significant challenge in vitreoretinal surgery, with reported incidence rates of 4.2–11.2% following standard vitrectomy with internal limiting membrane (ILM) peeling and gas tamponade. Risk factors include large hole size (>400 µm), chronicity (>6 months), high myopia, incomplete ILM peeling, and post-operative noncompliance. Multiple surgical techniques exist, though comparative evidence remains limited. Current options include the inverted ILM flap technique, autologous ILM transplantation (free flap or plug), lens capsular flap transplantation (autologous or allogenic), preserved human amniotic membrane transplantation, macular subretinal fluid injection, macular fibrin plug with autologous platelet concentrates, and autologous retinal transplantation. Closure rates range from 57.1% to 100%, with selection depending on hole size, residual ILM, patient posturing ability, etc. For non-posturing patients, fibrin plugs are preferred. Residual ILM cases may benefit from extended peeling or flap techniques, while large holes often require scaffold-based (lens capsule, amniotic membrane) or fibrin plug approaches. Pseudophakic patients should avoid posterior capsular flaps due to lower success rates. Despite promising outcomes, the lack of randomized trials necessitates further research to establish evidence-based guidelines. Personalized surgical planning, considering anatomical and functional goals, remains crucial in optimizing visual recovery in rFTMHs. Full article

This study introduces a novel model reference adaptive control (MRAC) framework that incorporates fractional-order gradients (FGs) to regulate the displacement of an inverted pendulum-cart system. Fractional-order gradients have been shown to significantly improve convergence rates in domains such as machine learning and neural network optimization. Nevertheless, their integration with fractional-order error models within adaptive control paradigms remains unexplored and represents a promising avenue for research. The proposed control scheme extends the classical MRAC architecture by embedding Caputo fractional derivatives into the adaptive law governing parameter updates, thereby improving both convergence dynamics and control flexibility. To ensure optimal performance across multiple criteria, the controller parameters are systematically tuned using a multi-objective Particle Swarm Optimization (PSO) algorithm. Two fractional-order error models (FOEMs) incorporating fractional gradients (FOEM2-FG, FOEM3-FG) are investigated, with their stability formally analyzed via Lyapunov-based methods under conditions of sufficient excitation. Validation is conducted through both simulation and real-time experimentation on a physical pendulum-cart setup. The results demonstrate that the proposed fractional-order MRAC (FOMRAC) outperforms conventional MRAC, proportional-integral-derivative (PID), and fractional-order PID (FOPID) controllers. Specifically, FOMRAC-FG achieved superior tracking performance, attaining the lowest Integral of Squared Error (ISE) of $2.32\times {10}^{-5}$ and the lowest Integral of Squared Input (ISI) of 6.40 in simulation studies. In real-time experiments, FOMRAC-FG maintained the lowest ISE ($5.11\times {10}^{-6}$). Under real-time experiments with disturbances, it still achieved the lowest ISE ($1.06\times {10}^{-5}$), highlighting its practical effectiveness. Full article

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

In high-frequency surface wave radar (HFSWR) systems, the retrieval of the directional wave spectrum has remained challenging, especially in the case of echoes from long ranges with a low signal-to-noise ratio (SNR). Therefore, a quadratic programming algorithm based on the regularization technique is proposed with an empirical criterion for estimating the optimal regularization parameter, which minimizes the effect of noise to obtain more accurate inversion results. The reliability of the inversion method is preliminarily verified using simulated Doppler spectra under different wind speeds, wind directions, and SNRs. The directional wave spectra inverted from a radar network with two multiple-input multiple-output (MIMO) systems are basically consistent with those from the ERA5 data, while there is a limitation for the very concentrated directional distribution due to the truncated second order in the Fourier series. Further, in the field experiment during a storm that lasted three days, the wave parameters are calculated from the inverted directional spectra and compared with the ERA5 data. The results are shown to be in reasonable agreement at four typical locations in the core detection area. In addition, reasonable performance is also obtained under the condition of low SNRs, which further verifies the effectiveness of the proposed inversion algorithm. Full article

Cross-validation and open research on future electric grids, particularly in their stability modeling and dynamic performance, can greatly benefit from high-fidelity, publicly available test cases, since access to dynamic response models of actual grid models is often limited due to legitimate security concerns. This paper presents a methodology for designing and validating the dynamic performance of large, IBR-heavy synthetic grids, that is, realistic but fictitious test cases. The methodology offers a comprehensive framework for creating dynamic models for both synchronous generators (SGs) and inverter-based resources (IBRs), focusing on realism, controllability, and flexibility. For realistic dynamic performance, the parameters in each dynamic model are sampled based on statistical data from benchmark actual grids, considering power system dynamics such as frequency and voltage control, as well as oscillation response. The paper introduces system-wide governor design, which improves the controllability of parameters in dynamic models, resulting in a more realistic frequency response. As an example, multiple case studies on a 2000-bus Texas synthetic grid are shown; these represent realistic dynamic performance under different transmission conditions in terms of frequency, voltage control, and oscillation response. Full article