Voltage Signal Research Articles

Super signal-enhancement biosensing platform for precise target recognition based on rolling circle-hybridization chain dual linear cascade amplification technology

This paper presents a self-powered biosensing platform based on graphdiyne@Au (2D GDY@Au) nanoparticles and rolling circle-hybridization chain (RC-HC) dual linear cascade amplification technology, which significantly enhances target recognition and signal amplification efficiency for miRNA-141. Specifically, the target on bioanode outputs a large amount of single-stranded DNA (T1) through the strand displacement amplification (SDA) mechanism. This efficient target recycling process triggers RC-HC dual linear cascade reaction. The RCA product and H2 form the L-Liner/H2 hybridized chain through a hybridization chain reaction, and then are immobilized on a flexible electrode using a Y-DNA capture handle. [Ru(NH3)6]3+ is precisely anchored in the grooves of the DNA double helix. The 2D GDY@Au enhances the electron mobility of the system to form a rich electron-donating center. The [Ru(NH3)6]3+ on the biocathode receives electrons and is reduced to [Ru(NH3)6]2+, producing a significantly amplified open-circuit voltage signal. Dual linear cascade amplification technology realizes precise target recognition, exponential amplification, and efficient conversion of biological signals. This technique displays an extensive linear range (0.0001–10000 pM) with a detection limit of 25.9 aM (S/N = 3), and it provides an innovative method for developing sensors based on nucleic acid amplification and presents a promising novel approach for the sensitive and precise detection of low-abundance target molecules, highlighting a new tactic for the creation of compact and portable analytical devices.

Opposite synaptic plasticity in oxidation-layer-controlled 2D materials-based memristors for mimicking heterosynaptic plasticity

Memristors with nonstoichiometric tungsten oxide (WOx) as an active layer, derived from the oxidation of atomically thin two-dimensional tungsten diselenide (WSe2), enable the creation of the monolithic layered structure of WOx/WSe2. These devices are promising candidates for emulating various biological synaptic functions in the human brain. In this study, we fabricate monolithic few-layer WOx/WSe2 memristors with precisely controlled WOx thickness by UV-ozone treatment from 1 L to 9 L, depending on chuck temperature. The postsynaptic responses of the topmost single-layer (1 L) oxidized WSe2 and fully (9 L) oxidized WSe2 memristors exhibit sharply contrasting behaviors, which can be applied to mimic the heterosynaptic plasticity in the CA1 region of the hippocampus. Beyond the significance of emulating the biological synaptic characteristics, we explore the feasibility of using each oxidation-layer-controlled memristor as a hardware accelerator. Their performances are assessed through application in a CIFAR-10 pattern recognition task using a convolutional neural network. Pattern recognition rates of 84 % and 71 % are obtained for the 1 L and 9 L WOx-based devices, respectively. We also examine the applicability of a synaptic cell composed of devices with oppositely switched characteristics. Consequently, the synaptic weight—defined as the difference in conductance between two synaptic devices—can be either increased (potentiated) or decreased (depressed) by simultaneously updating both devices with the same voltage signal. This weight update concept achieves a moderate recognition rate of 85.94 % when using an MNIST pattern-based recognition task, simplifying the complex weight-adjustment process.

Fault diagnosis of cells in PEM electrolyzer stack under fluctuating power source

The scale of hydrogen production using renewable energy is expanding with the global energy transition accelerating. Safety in hydrogen production is gaining increased attention. Compared to stable power supplies, the voltage of the electrolyzer stack fluctuates dynamically with input power in the wind and photovoltaic systems. For this reason, a fault diagnosis method for electrolyzers under fluctuating power sources is proposed. Firstly, the faults of the sensor and electrolyzer are distinguished by interleaved voltage detection to avoid the misdiagnosis caused by the sensor failure. Secondly, an improved variable mode decomposition method is introduced to process the raw voltage signals to weaken the influence of inter-cell inconsistency on fault diagnosis. Furthermore, the fault characteristics are highlighted with a shortened fault identification time. Then, the fault characteristics are extracted by the degree of cell voltage fluctuation with the occurrence of faults is judged by the difference in the degree of fluctuation between neighboring electrolyzers. Finally, faults such as short circuits and water shortages in the electrolyzer are diagnosed using the correlation differences in cell voltage between different faults. The feasibility of this fault diagnosis method is validated through case studies and comparative analyses.

Triboelectric Nanogenerator-Embedded Intelligent Self-Aligning Roller Bearing with the Capability of Self-Sensing, Monitoring, and Fault Diagnosis

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg−1·rpm−1. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis.

Improved direct ripple power predictive control of single-phase rectifier based on ripple separation

Voltage ripple is introduced to the DC link when a single-phase rectifier operates, which affects the energy balance of both the DC and AC sides. Accurate acquisition and fast, precise tracking of the decoupling capacitor voltage and decoupling inductor current reference values are challenges in the design of active power decoupling controllers. This article employs instantaneous ripple power control, shifting the focus from the accuracy of the decoupling capacitor voltage and decoupling inductor current reference values to the accuracy of the ripple power reference value. A ripple separation-based active power decoupling control strategy is proposed. By designing a time-varying observer, the amplitude feedback signal of the output voltage’s second-harmonic ripple is extracted in real time to generate the ripple power reference, enhancing its accuracy and reliability. The endpoint equivalent modulation method is adopted to track the instantaneous ripple power. Compared with traditional finite control set model predictive control, it achieves better tracking performance at the same control frequency, with a fixed switching frequency. Additionally, measures are proposed to address input current distortion caused by output voltage ripple entering the rectifier’s grid-side current control loop. This avoids the pollution of the input current by the output ripple voltage. Simulations and experimentations are performed to test the proposed control strategy.

Optimized galvanometric illumination for terahertz full-field imaging and computed tomography

The pursuit of high-resolution, high-fidelity, real-time imaging is receiving significant attention in terahertz community. In this study, a versatile illumination approach based on a double-mirror galvanometer is proposed and optimized for multiple terahertz imaging approaches. We analyzed the mechanism of galvanometric illumination and elucidated two main factors affecting its homogeneity and parallelism properties. In our module, the terahertz beam is deflected rapidly by the galvanometer which is driven by triangular voltage signals, and then focused by a self-designed aspherical f-θ lens to illuminate the object at an equal lateral scanning velocity. The object beam of different transients is periodically superimposed along the Lissajous trajectory, and is recorded by an array microbolometer in a single integration of 3 s. A homogeneous illumination field is realized with a speckle contrast of 0.11, and the resultant image achieves isotropic resolution. By adopting the proposed galvanometric illumination strategy, the average intensity of the illumination field is increased by 135% compared to expanded coherent illumination, and the speckle contrast is reduced by 83.5% compared to other galvanometric illumination. By virtue of leading low speckle noise, illumination homogeneity and parallelism, a compact imaging system is built for terahertz full-field imaging and computed tomography achieving high imaging speed and fidelity. As a successful attestation of terahertz beam steering, this study is very promising to reduce the total cost, increase the performance and expand the application of terahertz imaging.

Dual calcium-voltage optical mapping of regional voltage and calcium signals in intact murine RyR2-R2474S hearts

Abnormal regional variations in electrical and calcium homeostasis properties have been implicated in catecholaminergic polymorphic ventricular tachycardias (CPVT) attributable to abnormal RyR2-mediated store Ca2+ release, but their underlying mechanism have not been well explored in intact hearts. MethodsWe performed in vivo and ex vivo studies including high throughput mapping of Ca2+ transients (CaT) and transmembrane voltage (Vm) in murine wild-type (WT) and heterozygous RyR2-R2474S/+ hearts, before and during isoprenaline (ISO) challenge. ResultsISO-challenged RyR2-R2474S/+ showed increased incidence of arrhythmia accompanied by abnormal Ca2+ transients compared to WT. CaT duration (CaTD) in the LV apex amongst regions studied both before and during ISO challenge in both WT and RyR2-R2474S/+ ventricles. RyR2-R2474S/+ ventricles showed prolonged CaTD, both before and during isoprenaline (ISO) challenge. Conversely, action potential durations (APD) were the same in WT and RyR2-R2474S/+ ventricles and identically reduced by ISO challenge. RyR2-R2474S/+ showed Vm-CaT latencies at time to half decay, but not rise time to peak, which were significantly prolonged compared to WT in all ventricular regions examined with ISO challenge. Following burst pacing, ventricular localized concordant alternans in CaT and APD were readily observed in RyR2-R2474S/+ but not in WT mice. Such CaT and APD alternans occurred mostly semiannually in specific regions of the ventricular pre-occurrence of VT. ConclusionThe pro-arrhythmic RyR2-R2474S/+ phenotype in intact hearts thus directly parallels delayed regional CaT recovery properties and alteration of Vm-CaT latencies. Studies suggest that discordant localized calcium alternans are mechanistically responsible for action potential duration alternans and occurrence of VT in RyR2-R2474S/+ mice.

Research on the Smooth Switching Control Strategy of Electric Vehicle Charging Stations Based on Photovoltaic–Storage–Charging Integration

To facilitate seamless transitions between grid-connected and islanded modes in PV–storage–charging integration, an energy storage system converter is designated as the subject of investigation, and its operational principles are examined. Feed-forward decoupling, double closed-loop, constant-power (PQ), constant-voltage–constant-frequency (V/F), and constant-voltage charge and discharge control strategies are developed. The PQ and V/F control framework of the energy storage battery comprises an enhanced common current inner loop and a switching voltage outer loop. The current reference value output by the voltage outer loop and the voltage signal output by the current inner loop are compensated. The transient impact is reduced, and the smooth switching of the microgrid from the grid-connected mode to the island mode is realized, which significantly improves the power quality and ensures the uninterrupted charging of electric vehicles and the stable operation of the key load of the system. By constructing a simulation model of the photovoltaic energy storage microgrid on the MATLAB/Simulink platform, the practicability of the control strategy proposed in this paper is verified.

An Approach for Detecting Faulty Lines in a Small-Current, Grounded System Using Learning Spiking Neural P Systems with NLMS

Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This gap in reliable fault detection necessitates more advanced methodologies to improve system stability and safety. Here, a novel approach, using learning spiking neural P systems combined with a normalized least mean squares (NLMS) algorithm to enhance faulty line detection in small-current, grounded systems, is proposed. The proposed method analyzes the features of current and voltage signals, as well as active and reactive power, by separately considering their transient and steady-state components. To improve fault detection accuracy, we quantified the likelihood of a fault occurrence based on feature changes and expanded the feature space to higher dimensions using an ascending dimension structure. An adaptive learning mechanism was introduced to optimize the convergence and precision of the detection model. Simulation scheduling datasets and real-world data were used to validate the effectiveness of the proposed approach, demonstrating significant improvements over traditional methods. These findings provide a robust framework for faulty-line detection in small-current, grounded systems, contributing to enhanced reliability and safety in power system operations. This approach has the potential to be widely applied in power system protection and maintenance, advancing the broader field of intelligent fault diagnosis.

Neuro-Actuating Photonic Skin Enabled by Ion-Gel Transistor with Thermo-Adaptive Block Copolymer.

Despite significant progress in developing artificial synapses to emulate the human nervous system for bio-signal transmission, synapses with thermo-adaptive coloration and soft actuators driven by temperature change have seldom been reported. Herein, a photonic neuro-actuating synaptic skin is presented enabling thermoresponsive synaptic signal transmission, color variation, and actuation. First, a thermoresponsive display synapse is developed based on a 3-terminal ion-gel transistor with a poly (3,4-ethylene dioxythiophene):poly (styrene sulfonate) (PEDOT:PSS) semiconducting channel mixed with 2D titanium carbide (Ti3C2Tx) MXene and a thermo-adaptive 1D block copolymer (BCP) photonic crystal (PC) gate insulator. Temperature-dependent synaptic behavior is successfully observed in the ion-gel transistor with the corresponding structural colors, leading to a thermo-adaptive display synapse. The 3 × 3 arrays of thermo-adaptive display synapses with Joule heaters show that each pixel is controlled by the thermoresponsive structural color and synaptic output. The synaptic output current from the MXene ion-gel transistor can be converted and amplified to a voltage signal, which powers a soft actuator connected to the ion-gel display synapse and triggers temperature-dependent actuation related to the thermoresponsive synaptic performance. This study showcases a thermo-adaptive photonic neuro-actuating artificial skin that emulates muscle-combined neuronal human skin with visualization capability.

Bioinspired Tilted Magnetized Flakes as a Self-Powered and Antislip Smart Outsole for Healthcare Monitoring and Human-Machine Interaction.

Footwear smart devices capable of reliably capturing body actions and conveniently transmitting human-made information are of great interest to advance healthcare monitoring, human-machine interactions (HMIs), etc. while remaining challenging. Herein, we present a self-powered, antislip, and multifunctional smart outsole based on the gecko toe-inspired tilted magnetized flakes (TMFs) and underlying flexible coils. With the pressure-induced flake deflection and the built-in magnetic moment alignment, the TMF can produce a variable magnetic field to induce the voltage signals in coils for precise pressure perception and linear velocity sensing. The TMF-based smart outsole can thus serve as a real-time footwear recorder to monitor various body actions for exercise analysis and to track the abnormal landing speed for alerting potential injuries. The gecko toe-like flakes also enable the excellent antislip capability of the outsole with a much higher friction coefficient than the standard one of the low slip risk. By programming the magnetic moment alignments of the TMFs, a single-circuit outsole can further output multiple signals as encoded instructions for controlling the racing game. Along with excellent abrasion resistance and environmental immunity, the proposed outsole exhibits great potential as a convenient platform for reliable healthcare monitoring and efficient HMI.

Intelligent fault diagnosis in power distribution networks using LSTM-DenseNet network

This paper introduces a novel fault diagnosis method that combines DenseNet and Long Short-Term Memory (LSTM) networks. The DenseNet utilizes its unique dense block structure to detect subtle variations in three-phase voltage and zero-sequence current signals. In addition, the Squeeze-and-Excitation (SE) module is introduced in DenseNet. The SE module enhances DenseNet's feature representation by adapting the importance of each channel in the feature map. Furthermore, integrating the LSTM model enables capturing time-domain features of fault signals, enhancing the analysis of waveform changes and trends. These extracted features are subsequently fused in a cascaded manner, leveraging the strengths of both approaches to obtain a more comprehensive information representation. To better explain the capability of feature extraction in each part of the model, t-distributed Stochastic Neighbor Embedding (t-SNE) method is used for visual analysis. The proposed method is evaluated using two distribution network models, namely the 10 kV and IEEE34 networks, in simulation. The verification results indicate that the proposed method achieves exceptionally high accuracy in fault identification for both tested distribution network models, with rates of 99.87 % and 99.82 %, respectively, while also demonstrating robust performance in noisy environments. This performance surpasses that of other related methods, underscoring the enhanced effectiveness of our approach.

Dual-Modal Fusion PRI-SWT Model for Eddy Current Detection of Cracks, Delamination, and Impact Damage in Carbon Fiber-Reinforced Plastic Materials

Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a Parallel Real–Imaginary/Swin Transformer (PRI-SWT) model. Built using the Transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision Transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors—including the real–imaginary fusion layer, the number of layers, the window shift size, and the model’s scale—on the classification performance of the PRI-SWT model was systematically evaluated.

Enhancing power quality in distributed generation systems using three-phase shunt active power filter

The current paper presents a new reference current generation algorithm for shunt active power filter (SAPF) combined with distributed generation systems using the instantaneous power theory (IPT) which is organized in two main blocks. The first is the synchronization technique; we propose a new quasi open loop technique that combines a Decoupled double self-tuning filter (DDSTF) and a single-phase frequency locked loop (FLL). The DDSTF is a new technique that extracts and separates the positive and negative sequence voltage under highly unbalanced and distorted conditions with no need to the symmetrical component and/or signals delay. The FLL achieve the grid frequency adaptation. Unlike the existing frequency estimators, this technique is motivated by its simplicity and the immunity to the voltage signal amplitude and phase variation or disturbance, which is a great advantage. The second is an ADALINE in power space to separate the fundamental harmonic from the distortion harmonics of the measured currents. The effectiveness of the proposed OLS technique in their application to the SAPF is validated by simulation.

Spin conduction and interfacial effects in La2/3Sr1/3MnO3/NiO/Pt heterostructures

In this work we report on the generation and transport of pure spin currents in La2/3Sr1/3MnO3(LSMO)/NiO/Pt heterostructures. Pure spin currents, generated by means of spin pumping in the LSMO layer, are transmitted through the NiO layer and detected by measuring the transverse voltage signal generated by the inverse spin Hall effect (ISHE) in the Pt layer. The spin current transmission is studied as a function of the NiO thickness and temperature. NiO layers grown at room temperature are polycrystalline and their microstructural and magnetic features clearly depend on thickness. Scanning transmission electron microscopy techniques revealed that homogeneous conformal coating of the LSMO layer is only achieved for NiO layer thicknesses above about 2 nm. Below this critical thickness the NiO layer is discontinuous and its main role would be to disturb the LSMO/Pt interface causing it to behave as something similar to an insulating barrier. Magnetic measurements indicate that NiO layers are paramagnetic (PM) well below room temperature. In agreement with these observations, the amplitude of the ISHE voltage signal decreases monotonically on lowering temperature and makes evident that there are two spin conduction regimes as a function of the NiO layer thickness with different spin diffusion lengths. For thin discontinuous NiO layers a spin diffusion length of λSDL≈0.8 nm is found, which is slightly larger than typical values found for insulating barriers, but definitely smaller than λSDL≈3.8 nm found for NiO layer thickness above 2 nm. The latter being similar to λSDL previously reported for epitaxial NiO layers. Our findings indicate that inserting a PM NiO insulating layer of optimal thickness improves the spin transparency of the LSMO/Pt interface. Additionally, the results suggest that spin conduction through the NiO layer is likely driven by magnetic correlations and short-range thermal magnons.

Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning.

Classifications of fluids using miniaturized sensors are of substantial importance for various fields of application. Modified with functional nanomaterials, a moisture-enabled electricity generation (MEG) device can execute a dual-purpose operation as both a self-powered framework and a fluid detection platform. In this study, a novel intelligent self-sustained sensing approach was implemented by integrating MEG with deep learning in microfluidics. Following a multilayer design, the MEG device including three individual units for power generation/fluid classification was fabricated in this study by using nonwoven fabrics, hydroxylated carbon nanotubes, poly(vinyl alcohol)-mixed gels, and indium tin bismuth liquid alloy. A composite configuration utilizing hydrophobic microfluidic channels and hydrophilic porous substrates was conducive to self-regulation of the on-chip flow. As a generator, the MEG device was capable of maintaining a continuous and stable power output for at least 6 h. As a sensor, the on-chip units synchronously measured the voltage (V), current (C), and resistance (R) signals as functions of time, whose transitions were completed using relays. These signals can serve as straightforward indicators of a fluid presence, such as the distinctive "fingerprint". After normalization and Fourier transform of raw V/C/R signals, a lightweight deep learning model (wide-kernel deep convolutional neural network, WDCNN) was employed for classifying pure water, kiwifruit, clementine, and lemon juices. In particular, the accuracy of the sample distinction using the WDCNN model was 100% within 15 s. The proposed integration of MEG, microfluidics, and deep learning provides a novel paradigm for the development of sustainable intelligent environmental perception, as well as new prospects for innovations in analytical science and smart instruments.

Extraction of two-magnon scattering and detection of inverse spin Hall effect up to 40 GHz in low-damping Fe65Co35/Pt bilayer thin film

Abstract We report an investigation of the inverse spin Hall effect (ISHE), as a measure of the pure spin current (J S ), induced by spin pumping in a Fe65Co35/Pt bilayer using coplanar waveguide ferromagnetic resonance (CPW-FMR) in the in-plane (IP) geometry over the broadband microwave frequency range of 8–40 GHz. From the IP-FMR measurements, the defect-induced two-magnon scattering (TMS) contribution is evaluated by analyzing the frequency dependence of FMR linewidth using Arias and Mills approach [Arias R and Mills D L (1999 Phys. Rev. B 60, 7395), Mills D L and Arias R (2006 Physica B 384 147–151)]. Here, we have determined a very low Gilbert damping constant (α G ) for Fe65Co35 bare film around 1.3 × 10−3, which is essential for the high spin current (>107 A/m2) generation. The enhanced value of α G ≈3.2 × 10−3 obtained for Fe65Co35/Pt bilayer film is due to spin pumping damping, α SP = 1.9 × 10−3, confirmed by the ISHE induced DC voltage (V DC ) signal measured across the edges of Pt layer. Out-of-plane (OP) angular ISHE measurements have been performed to disentangle spin pumping from spin rectification effects in the V DC signal. Further, we evaluated the effective spin-mixing conductance g eff ↑ ↓ = 3.33 × 1019 m−2, which is higher than the values reported in FeCo-alloy-based bilayer systems. The present study aims to detect the J S injected by low-damping Fe65Co35 thin films, leading to the realization of energy-efficient spintronic devices.

A Hybrid Data-Driven Method for Main Circuit Gound Faults Diagnosis in Electrical Traction Drive Systems

A main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operation and ensure driving safety, this paper proposes a hybrid data-driven MCGF diagnosis method. First, the voltage signals related to the fault are selected according to the mechanism analysis of the MCGF, and then the initial feature variables are constructed according to these voltage signals. Secondly, the initial feature variables of different types of MCGF are analyzed in the time and frequency domains by wavelet transform, and four feature indicators are calculated. Finally, the fault feature indicators are trained by random forest to obtain a model for subsequent fault diagnosis. After comparative experiments using various machine learning methods, it was found that the RF used in the proposed method has a better diagnostic effect, and the correct isolation rate exceeds 99%.

Wavelet-Based ensembled intelligent technique for advanced fault detection and classification in AC microgrids

This research introduces a novel intelligent protection technique for microgrids, leveraging Discrete Wavelet Transform (DWT) and an Ensemble Bagged Decision Tree (EBDT) classifier for precise fault detection and classification across various scenarios. Non-stationary voltage and current signals are analysed using DWT to extract wavelet detailed coefficients, which are then used to compute the energy of these coefficients as input features for the EBDT. The hyperparameters of the EBDT are optimized using a random search algorithm to enhance robustness in fault classification. The proposed protection scheme’s efficacy is validated on a modified IEC test microgrid model, incorporating both inverter-interfaced Distributed Generators (DGs) and synchronous DGs, under different fault conditions simulated in the MATLAB/SIMULINK environment. Results demonstrate that the method is both fast and accurate, achieving fault detection and classification accuracies of 100% in both grid-connected and islanded modes of operation. The performance of the proposed model is benchmarked against state-of-the-art methods, including Decision Tree and Random Forest classifiers. Additionally, the robustness of the proposed technique is confirmed under conditions of DG uncertainty and in the presence of noise. The effectiveness of the technique is assured across various fault conditions, and it is further validated in an OPAL-RT real-time environment. This study significantly enhances the reliability and resilience of microgrid protection systems, offering a robust solution for real-time fault management, which is crucial for the stability of modern power networks integrating renewable energy sources.

Scalable Multispecies Ion Transport in a Grid-Based Surface-Electrode Trap

Quantum processors based on linear arrays of trapped ions have achieved exceptional performance, but scaling to large qubit numbers requires realizing two-dimensional ion arrays as envisioned in the quantum charge-coupled device (QCCD) architecture. Here, we present a scalable method for the control of ion crystals in a grid-based surface-electrode Paul trap and characterize it in the context of transport operations that sort and reorder multispecies crystals. By combining cowiring of control electrodes at translationally symmetric locations in each grid site with the sitewise ability to exchange the voltages applied to two special electrodes gated by a binary input, site-dependent operations can be achieved using only a fixed number of analog voltage signals and a single digital input per site. In two separate experimental systems containing nominally identical grid traps, one using Yb+171−Ba+138 crystals and the other Ba+137−Sr+88, we demonstrate this method by characterizing the conditional intrasite crystal reorder and the conditional exchange of ions between adjacent sites on the grid. Averaged across a multisite region of interest, we measure subquanta motional excitation in the axial in-phase and out-of-phase modes of the crystals following these operations at exchange rates of 2.5 kHz. In this initial demonstration, the logic controlling the voltage exchange occurs in software, but the applied signals mimic a proposed hardware implementation using crossover switches. These techniques can be further extended to implement other conditional operations in the QCCD architecture such as gates, initialization, and measurement. Published by the American Physical Society 2024