Search Results (696)

Social media provides a vital arena for rare disease (RD) communities, fostering support, advocacy, and knowledge sharing. Rare Disease Day generates a large-scale online conversation, yet previous research has relied mainly on static, cross-sectional snapshots. This study captures the longitudinal evolution of the Spanish-language Twitter debate around Rare Disease Day across a fixed yearly window (1 February to 15 March) from 2008 to 2023. After filtering for Spanish-language posts, a corpus of 308,823 tweets (72,740 originals) was analyzed. We combined hashtag frequency analysis to assess topic salience with social network analysis (SNA) of co-occurrence networks to identify central thematic clusters. Results show progression from early generic expressions to increasingly deliberate, action-oriented communication, reflecting a shift towards empowered activism. A headline finding is the structural centrality and persistence of the hashtag #investigación (#research), underscoring the community’s enduring call for scientific progress. SNA further revealed the difference between transient virality—often linked to political or celebrity-driven hashtags—and the stable, identity-related topics at the core of the debate. Longitudinal hashtag analysis, particularly using SNA, provides a powerful tool to identify stable priorities of online health communities beyond transient media noise. Full article

This paper presents a new compact and efficient first-order all-pass filter in voltage mode based on a second-generation voltage conveyor, along with two resistors, and a capacitor. This circuit delivers an all-pass response from the low-impedance node and eliminates the need for a voltage buffer in cascading configurations. A thorough non-ideal analysis, accounting for parasitic impedances and the non-ideal gains of the active module, shows negligible effects on the filter performance. Furthermore, a sensitivity analysis with respect to both active and passive components further validates the robustness of the design. The proposed all-pass filter is validated by Cadence PSPICE simulations, utilizing 0.18 µm TSMC CMOS process parameter and ±0.9 V power supply, including Monte Carlo analysis and temperature variations. Additionally, experimental validation is carried out using commercially available IC AD844, showing great consistency between theoretical and experimental results. Resistor-less realization of the proposed filter provides tunability feature. A quadrature sinusoidal oscillator is presented to validate the proposed structure. The introduced circuit provides a simple and effective solution for low-power and compact analog signal processing applications. Full article

Due to the “low damping” characteristics of the DC distribution system, the traditional passive scheme is not suitable for DC fault detection and positioning. Therefore, this paper proposes an active injection fault identification method suitable for DC feeder line under single-pole grounding faults. Based on the high controllability of converters, this method uses the oscillation circuit characteristics of the DC side single-pole grounding fault to superimpose the harmonics of fixed frequency into the converter modulated wave, and derives the selection principles of harmonic amplitude and frequency. After the fault, the positive and negative current signals are extracted from the feeder lines, and the zero-mode current components are extracted by the Karrenbauer transformation and band-pass filter, the current phases are compared to achieve the fault feeder line selection. According to simulation verification, the power quality of the actively injected harmonics is within the standard range under the condition of global injection, and the single-pole grounding faults in each feeder line can be identified. Full article

Under China’s dual-carbon strategic objectives, virtual power plants (VPPs) actively participate in the coupled electricity–carbon market through the optimized scheduling of distributed energy resources, simultaneously stabilizing grid operations and reducing carbon emissions. Photovoltaic (PV) generation, a cornerstone resource within VPP systems, introduces significant challenges in scheduling due to its inherent output variability. To increase the accuracy in the characterization of the PV output uncertainty, a weighted probability distribution of solar irradiance, based on historical irradiance data, is newly proposed. The leveraging rejection sampling technique is applied to generate solar irradiance scenarios that are consistent with the proposed weighted solar irradiance probability model. Further, a confidence interval-based filtering mechanism is applied to eliminate extreme scenarios, ensuring statistical credibility and enhancing practicability in actual dispatch scenarios. Based on the filtered scenarios, a novel dispatch strategy for the VPP operation in the electricity–carbon market is proposed. Numerical case studies verify that scenarios generated by the weighted solar irradiance probability model are capable of closely replicating historical PV characteristics, and the confidence interval filter effectively excludes improbable extreme scenarios. Compared to conventional normal distribution-based methods, the proposed approach yields dispatch solutions that are more closely aligned with the optimal dispatch of the historical irradiance data, demonstrating the improved accuracy in the probabilistic modelling of the PV output uncertainty. Consequently, the obtained dispatch strategy shows the improved capability to ensure the market revenue of the VPP considering the fluctuations of the PV output. Full article

This paper presents a low-power continuous-time delta-sigma (CT-DS) analog-to-digital converter (ADC) for use in high-sensitivity automotive-grade sub-1 GHz receivers in emerging wireless sensors network applications. The proposed ADC employs a third-order Cascade of Integrators FeedForward and Feedback (CIFF-B) loop filter operating at a sampling frequency of 150 MHz to achieve high energy efficiency and robust noise shaping. A low-noise phase-locked loop (PLL) is integrated to provide high-precision clock signals. The loop filter combines active-RC and GmC integrators with the source degeneration technique to optimize power consumption and linearity. To minimize complexity and enhance stability, a 1-bit quantizer with isolation switches and return-to-zero (RZ) digital-to-analog converters (DACs) are used in the modulator. With a 500 kHz bandwidth, the sensitivity of the receiver is −105.5 dBm. Fabricated in a 180 nm standard CMOS process, the prototype achieves a peak signal-to-noise ratio (SNR) of 76.1 dB and a signal-to-noise and distortion ratio (SNDR) of 75.3 dB, resulting in a Schreier figure of merit (FoM) of 160.7 dB based on SNDR, while consuming only 0.8 mA from a 1.8 V supply. Full article

This work presents a high-common-mode-rejection-ratio (CMRR) and high-gain FinFET-based bio-potential amplifier with a novel CMRR reduction technique. In this paper, a feedback buffer is used alongside a capacitively coupled chopper-stabilized circuit to reduce the common-mode signal gain, thus boosting the overall CMRR of the circuit. The conventional pseudoresistor in the feedback circuit is replaced with a tunable parallel-cell configuration of pseudoresistors to achieve high linearity. A chopper spike filter is used to mitigate spikes generated by switching activity. The mid-band gain of the chopper-stabilized amplifier is 42.6 dB, with a bandwidth in the range of 6.96 Hz to 621 Hz. The noise efficiency factor (NEF) of the chopper-stabilized amplifier is 6.1, and its power dissipation is 0.92 µW. The linearity of the parallel pseudoresistor cell is tested for different tuning voltages (V_tune) and various numbers of parallel pseudoresistor cells. The simulation results also demonstrate the pseudoresistor cell performance for different process corners and temperature changes. The low cut-off frequency is adjusted by varying the parameters of the parallel pseudoresistor cell. The CMRR of the chopper-stabilized amplifier, with and without the feedback buffer, is 106.9 dB and 100.3 dB, respectively. The feedback buffer also reduces the low cut-off frequency, demonstrating its multi-utility. The proposed circuit is compatible with bio-signal acquisition and processing. Additionally, a machine learning-based arrhythmia diagnosis model is presented using a convolutional neural network (CNN) + Long Short-Term Memory (LSTM) algorithm. For arrhythmia diagnosis using the CNN+LSTM algorithm, an accuracy of 99.12% and a mean square error (MSE) of 0.0273 were achieved. Full article

Adaptive Linear Neuron (ADALINE) is a well-known neural network method that has been utilized for generating a reference current intended to regulate the operation of shunt-typed active harmonic filters (SAHFs). These filters are essential for improving power quality by mitigating harmonic disturbances and restoring current waveform symmetry in power systems. While the latest variant, Simplified ADALINE, offers notable advantages over its predecessors, such as a reduced complexity and faster learning speed, its performance has primarily been evaluated under stable grid conditions, leaving its performance under distorted environments largely unexplored. To address this gap, this work introduces two key modifications to the Simplified ADALINE framework: (1) the integration of a new phase-tracking algorithm based on the concept of orthogonality and selective filtering, and (2) transitioning from the direct current control (DCC) to an indirect current control (ICC) mechanism. Test environments featuring distorted grids and nonlinear rectifier loads are simulated in MATLAB/Simulink software to evaluate the performance of the proposed method against the existing Simplified ADALINE method. The key findings demonstrate that the proposed method effectively handled harmonic distortion and noise disturbance. As a result, the associated SAHF achieved an additional reduction in %THD (by 10.77–13.78%), a decrease in reactive power (by 58.3 VAR–67 VAR), and improved grid synchronization with a smaller phase shift (by 0.9–1.2°), while also maintaining proper waveform symmetry even in challenging grid conditions. Full article

Secondary control (SC) under false data injection attacks (FDIAs) in microgrids can compromise control decisions and disrupt the normal operation of the system. This paper proposes a washout-filter power-sharing-based resilient control strategy to tackle FDIAs. This strategy ensures the primary control continues to function normally by enabling the timely disconnection of the attacked SC. To address the under-rated operation state caused by the loss of SC, washout-filter power sharing is activated to restore the rated operation. Furthermore, for the FDIAs that affect both system frequency and voltage simultaneously after power sharing, a voltage compensation control loop is designed for the local voltage drop, allowing the attacked voltage value to further recover to the rated value. This strategy secures a steady frequency and enhanced voltage amplitude in the system, achieving a resilient effect against FDIAs. The proposed strategy has been validated through various simulation scenarios and FPGA-in-the-loop experiments. Full article

For the high-voltage AC port of power electronic transformer (HVAC-PET) with three-phase independent DC buses on the low-voltage side, a decoupling control strategy, concerning the influence of grid voltage imbalance, three-phase active-load imbalance, and high-order harmonic distortion, is proposed in this paper to simultaneously realize the functions of active power control, reactive power compensation, and active power filtering. In the outer power control loop, according to the distribution rule of decoupled average active power components in three phases, stability control for the sum of cluster average active power flows is realized by injecting positive-sequence active current, so as to control the average cluster voltage (i.e., the average of all the DC-link capacitor voltages), and by injecting negative-sequence current, the cluster average active power flows can be controlled individually to balance the three cluster voltages (i.e., the average of the DC-link capacitor voltages in each cluster). The negative-sequence reactive power component is considered to realize the reactive power compensation. In the inner current control loop, the fundamental and high-order harmonic components are uniformly controlled in the positive-sequence dq frame using the PI + VPIs (vector proportional integral) controller, and the harmonic filtering function is realized while the fundamental positive-sequence current is adjusted. Experiments performed on the 380 V/50 kVA laboratory HVAC-PET verify the effectiveness of the proposed control strategy. Full article

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise control (ANC) algorithm that improves both the modeling capability of nonlinear acoustic paths and the convergence performance of the system. The proposed approach integrates the nonlinear representation power of the Volterra filter with a momentum optimization mechanism to enhance convergence speed while maintaining robust steady-state accuracy. Simulations are conducted under second- and third-order nonlinear primary paths, followed by performance validation using multi-tone signals and real in-cabin tractor noise recordings. The results demonstrate that the proposed algorithm achieves superior performance, reducing the NMSE to approximately −35 dB and attenuating residual noise energy by 3–5 dB in the 200–800 Hz range, compared to FXLMS and VFXLMS algorithms. The findings highlight the algorithm’s potential for practical implementation in nonlinear and narrowband active noise control scenarios within complex mechanical environments. Full article

The quality of a grid-injected current in LCL-type grid-connected inverters (GCI) degrades under ultra-weak grid conditions, posing serious challenges to the stability of the GCI system. For this purpose, the sliding mode control (SMC) approach has been utilized to integrate DC energy seamlessly into the grid. The control performance of a GCI equipped with an LCL filter is greatly reduced when it is operating in a power grid with varying impedance and fluctuating grid voltages, which may result in poor current quality and possible instability in the system. A non-singular double integral terminal sliding mode (DIT-SMC) control is presented in this paper for a three-phase GCI with an LCL filter. The proposed method is presented in the α, β frame of reference without adopting an active or passive damping approach, reducing the computational burden. MATLAB/Simulink Version R2023b is leveraged to simulate the mathematical model of the proposed control system. The capability of the DIT-SMC method is validated through the OPAL-RT hardware-in-loop (HIL) platform. The effectiveness of the proposed method is first compared with SMC and integral terminal SMC, and then the DIT-SMC method is rigorously analyzed under resonance frequency events, grid impedance variation, and grid voltage distortions. It is demonstrated by the experimental results that the proposed control is highly effective in delivering a high-quality current into the grid, in spite of the simultaneous occurrence of power grid impedance variations in 6 mH and large voltage distortions. Full article

Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R² while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R² ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article

With the increasing penetration of distributed generation and the diversification of electrical equipment, distribution networks face issues like three-phase unbalance and harmonic currents, while the voltage stability and inertia of the grid-connected system also decrease. A certain amount of energy storage is needed in a Distribution Static Synchronous Compensator (DSTATCOM) to manage power quality and actively support voltage and inertia in the network. This paper first addresses the limitations of traditional $dq0$ compensation algorithms in effectively filtering out negative-sequence twice-frequency components. An improved $dq0$ compensation algorithm is proposed to reduce errors in detecting positive-sequence fundamental current under unbalanced three-phase conditions. Second, considering the impedance ratio characteristics of the distribution network, while reactive power voltage regulation is common, active power regulation is more effective in high-resistance distribution networks. A grid-forming model-based active and reactive power coordinated voltage regulation method is proposed. This method uses synchronous control to establish a virtual three-phase voltage internal electromotive force, forming a comprehensive compensation strategy that combines power quality improvement and active voltage support, exploring the potential of energy storage DSTATCOM applications in distribution networks. Finally, simulation and experimental results demonstrate the effectiveness of the proposed control method. Full article

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

Focusing on the common problems of phase-locked loop dependence, multiple current sensor requirements, a large number of controllers, and complex settings in traditional parallel active power filter (APF) control methods, this paper proposes a harmonic compensation control strategy based on an improved proportional resonant (PR) controller. The proposed method introduces an instantaneous power theory to construct a reference current model, which relies solely on grid voltage and current signals, does not require load-side current detection and phase-locked loop modules, and effectively simplifies the sensor configuration and system structure. At the same time, compared with the traditional solution that requires PR modules to be configured for each order of harmonics, this study only uses one set of PR controllers for fundamental current tracking, which has advantages in terms of compactness and computing resource occupation. To guide the controller parameter setting, this paper systematically discusses the influence of changes in K_p and K_r on pole distribution and dynamic performance based on discrete domain modeling and root locus analysis methods. The results were verified on the MATLAB/Simulink simulation platform and the 1 kVA experimental platform and compared with the traditional control method that requires the use of phase-locked loops (PLLs), load current sensors, and multiple PR controllers. The simulation and experimental results show that the proposed method has achieved a certain degree of optimization in terms of harmonic suppression effect, dynamic response performance, and system structure complexity. Full article