Search Results (61)

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

Solid amine adsorbents can capture CO₂ at indoor-relevant concentrations (~1000 ppm), but many high-capacity adsorbents rely on granular or powdery supports that are difficult to integrate directly into air purification systems. Here, we applied three amination strategies to commercial fibrous substrates: bridge-grafting on viscose (TEPA-AMVF), direct grafting on polyacrylonitrile (TEPA-PAN), and physical impregnation on pore-engineered activated carbon fibre felt (PEI-ACF). These adsorbents were systematically screened under simulated indoor conditions (1000 ppm CO₂, 27 °C, 50% RH). A significant capacity difference was observed: TEPA-AMVF (24.8 mg g⁻¹) < TEPA-PAN (35.8 mg g⁻¹) ≪ PEI-ACF (97.0 mg g⁻¹). The superior performance of PEI-ACF was attributed to KOH activation, which produced a mesopore-rich structure (average pore diameter 26.1 nm at an optimal KOH/carbon ratio of 1.25) and enabled high nominal amine utilisation (0.19 mmol CO₂ mmol N⁻¹). PEI-ACF maintained high breakthrough-derived CO₂ uptake across realistic indoor conditions (64.2–118.6 mg g⁻¹ over 0%–100% RH; 71.6–124.5 mg g⁻¹ over 400–5000 ppm CO₂), exhibited rapid kinetics (pseudo-first-order rate constant k = 1.77 h⁻¹; 81.7% of equilibrium uptake within 1 h), and showed stable but partial regeneration over four adsorption–desorption cycles at 60–70 °C under N₂. Compared with granular or resin-based amine sorbents, the self-supporting PEI-ACF felt is expected to offer practical advantages for filter-integrated CO₂ removal, including mechanical integrity under airflow, reduced risk of particle leakage, and compatibility with HVAC filter slots. Remaining challenges include direct pressure-drop validation, operation in O₂-containing indoor air, long-term cycling, and management of CO₂ released during regeneration. Full article

This paper investigates harmonic compensation for an LCL-filtered cascaded H-bridge (CHB) STATCOM operating in medium-voltage distribution networks under grid-frequency deviations and nonlinear loads. A hybrid current control strategy is proposed by combining a deadbeat (DB) inner-current loop with a Thiran all-pass filter-based frequency-adaptive repetitive controller (FARC). Weighted average inductor current (WAIC) feedback is adopted to reduce the third-order LCL filter to an equivalent first-order plant, thereby simplifying the current loop design while retaining the dominant low-frequency dynamics. The Thiran all-pass fractional delay filter is then embedded in the repetitive controller to realize a noninteger-period internal model at a fixed sampling frequency. This enables the controller to maintain harmonic compensation accuracy when the grid frequency deviates from its nominal value. A 10 kV LCL-filtered CHB STATCOM model is developed in MATLAB/Simulink, and the proposed method is compared with a conventional repetitive controller (CRC) under nominal frequency, frequency drift, nonlinear loading, harmonic load-switching conditions and grid impedance variation. Simulation results show that the proposed controller reduces the grid-current THD from 4.35% to 3.88% at 50 Hz, from 5.20% to 2.37% at 49.6 Hz, and from 6.51% to 3.56% at 50.4 Hz. In the tested frequency range of 49.5–50.5 Hz, the proposed method also maintains the power factor close to unity. These quantitative results demonstrate improved frequency robustness, harmonic suppression, and current-tracking performance compared with the CRC scheme, indicating that the proposed method can enhance STATCOM-based power quality compensation and support more reliable and efficient operation of medium-voltage distribution networks. Full article

Accurate ultra-short-term power forecasting for distributed photovoltaic (DPV) systems is crucial for the intra-day operation of distribution networks. However, the current method based on a graph network only takes a few DPV sites as forecasted objects; when modeling a large number of DPV objects, the massive graph structure will require multiple instances of information propagation to achieve global correlation extraction. Due to the similar output characteristics of adjacent DPV sites, excessive information aggregation will lead to node features tending towards consistency, making information extraction inefficient and insufficient, which limits the improvement of forecasting accuracy. To address the issues above, this study proposes an ultra-short-term distributed PV power forecasting method considering spatiotemporal correlation. First, the DPV sites are clustered into several sub-regions in different layers considering the spatial location of DPV sites and the temporal characteristics of power output. And a hierarchical architecture is constructed from DPV sites to sub-regions based on subordinate relationship and the order of information transmission. After that, the output mode of every sub-region is dynamically described in refinement by filtering out the noise DPV sites with significant differences in outputs. Finally, by hierarchically and sequentially mining the local and global spatiotemporal correlation among output modes, the hierarchical dynamic graph convolutional network is applied to achieve the regional power forecasting. Experimental results based on data from 166 DPV sites demonstrate that the proposed HDGCN model significantly outperforms the best traditional benchmark model, reducing the Normalized Root Mean Square Error (NRMSE) by approximately 38.56% and the Normalized Mean Absolute Error (NMAE) by 33.79% in a 4 h-advance forecasting scale. Full article

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, ${\mathcal{H}}_{\infty }$, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

With the rapid advancement of offshore wind power, structural vibration induced by multi-source excitations in complex marine environments is a critical concern. This study developed a multi-degree-of-freedom (MDOF) dynamic model of an offshore wind turbine using a lumped mass approach, which was then reduced to a first-order linear system to improve frequency-domain analysis and optimization efficiency. Given the non-stationary, broadband nature of wind and wave loads, a band-pass filtering technique was applied to extract dominant frequency components, enabling linear modeling of excitations within primary modal ranges. The displacement response spectrum, derived via system transfer functions, served as the objective function for optimizing tuned mass damper (TMD) parameters. Both single TMD and multiple TMD (MTMD) strategies were designed and compared. A hierarchical particle swarm optimization (H-PSO) algorithm was proposed for MTMD tuning, using the optimized single TMD as an initial guess to enhance convergence and stability in high-dimensional spaces. The results showed that the frequency-domain optimization framework achieved a balance between accuracy and computational efficiency, significantly reducing structural responses in dominant modes and demonstrating strong potential for practical engineering applications. Full article

Photoacoustic spectroscopy is a promising method for detecting dissolved acetylene (C₂H₂) in transformer oil, facilitating early fault diagnosis in power transformers. However, temperature variations significantly influence the resonance frequency of the photoacoustic cell, potentially reducing detection accuracy. This study investigates the temperature effects on the first-order longitudinal acoustic mode of a resonant photoacoustic cell using finite element simulations with thermo-viscous acoustics. The results show that as the temperature increases, the resonant frequency increases linearly and the sound pressure amplitude decreases, consistent with analytical models. To enhance system robustness, a perturbation observation method is proposed, treating operating frequency as the independent variable and acoustic pressure as the dependent variable. Time-domain simulations validate its effectiveness in tracking resonance frequency shifts under varying temperatures, ensuring reliable detection. Future work should focus on improving frequency resolution, noise filtering, and adaptive step-size optimization for practical applications. Full article

Based on a fractional-order equivalent circuit model, this paper proposes a multi-timescale collaborative State of Energy (SOE) and State of Health (SOH) estimation method (FOASTFREKF-EKF) for lithium batteries to mitigate the influence of model inaccuracies and battery aging on SOE estimation. Initially, a fractional-order equivalent circuit model is built, and its parameters are identified offline using the Starfish Optimization Algorithm (SFOA) to establish a high-fidelity battery model. An H∞ filter is then integrated to improve the algorithm’s resilience to external disturbances. Furthermore, an adaptive noise covariance adjustment mechanism is employed to reduce the effect of operational noise, and a time-varying attenuation factor is introduced to improve the algorithm’s tracking and convergence capabilities during abrupt system-state changes. A joint estimator is subsequently constructed, which uses an Extended Kalman Filter (EKF) for the online determination of battery parameters and SOH assessment. This approach minimizes the effect of varying model parameters on SOE accuracy while reducing computational load through multi-timescale methods. Experimental validation under diverse operating conditions shows that the proposed algorithm achieves root mean square errors (RMSE) of less than 0.21% for SOE and 0.31% for SOH. These findings demonstrate that the method provides high accuracy and reliability under complex operating conditions. Full article

Background: Primary hepatocytes are excellent models for studying liver functions and liver diseases. However, obtaining high yields of viable hepatocytes remains technically challenging, limiting their broader applications. Most conventional methods rely on a two-step collagenase perfusion technique. Despite its widespread use, this approach has several limitations that reduce the success rate of hepatocyte isolation and culture. The procedure involves multiple parameters that are continually being optimized in order to obtain hepatocytes in high yield and quality that can be used to provide insights into their physiology and pathophysiology. Aim: We aimed to enhance the success rate and reproducibility of hepatocyte isolation with high yield, enabling analysis of diverse physiological and pathophysiological aspects of lipid metabolism. It also establishes an in vitro steatosis model for evaluating therapeutic drugs and molecular interventions. Methods: Rat liver was perfused in situ with EDTA buffer followed by collagenase IV. Liver was then isolated, and hepatocytes were mechanically liberated, filtered, and purified through density-gradient centrifugation. Viable cells were cultured at 700,000 or 1 million cells/well for 24 h. The monolayer was incubated in lipogenic media for an additional 24 or 48 h. Hepatocytes were fixed, neutral lipids were stained using Oil Red O, and the stained area was quantified using Image J software version 1.54. Results: Yield of hepatocytes was ~75–90 million cells/liver, with viability of 86–93%. Cells seeded at 700,000 and 1 million cells/well reached confluences of 60% and 80%, respectively, after 24 h. Steatosis was then induced with lipid accumulation reaching 21% of image area after 24 h and 25% after 48 h. Conclusions: The current protocol presents an efficient and highly reproducible method for isolating primary rat hepatocytes in high yield with high viability. Additionally, the protocol provides a foundation for studying the pathophysiology of fatty liver disease. Full article

This paper presents our research on an E-band Radio Frequency (RF) front-end packaging structure based on back-to-back gap waveguide (GW). This design effectively mitigates the impact of air gaps on performance and offers the advantage of large assembly tolerances. Additionally, its back-to-back structure enables structural stacking, which can reduce the overall packaging size. In terms of functionality, the structure integrates hybrid couplers, bandpass filters, and amplifier packaging structures. Notably, the hybrid couplers provide high port isolation, facilitating a higher isolation duplex function by simply connecting high-order bandpass filters at the output ports without the need for additional optimization. Furthermore, these couplers also serve as power dividers/combiners. When combined with the H-plane amplifier packaging structures, the output power of the module is theoretically increased by 3 dB. Based on the measurements, the results indicate that this structure operates within the frequency ranges of 71–76 GHz and 81–86 GHz. The common port return loss is below 12 dB, while the in-band insertion loss is less than 2.26 dB and 2.42 dB, respectively. These findings demonstrate excellent electrical performance and suitability for E-band communication systems. Full article

The mining of relevant association rules from transactional databases is a fundamental process in data mining. Traditional algorithms, however, will typically be based on fixed thresholds and general rule generation, with the result being large and redundant outcomes. This paper presents DERAR (Dynamic Extracting of Relevant Association Rules), a dynamic approach integrating structure pattern mining and dynamic multi-criteria filtering. The process begins with the generation of frequent meta-patterns. Each entity is given a stability score for its consistency across various data projections, then sorted by mutual information in order to preserve the most informative dimensions. The resulting association rules from these models are filtered through a dynamic confidence threshold that is adjusted according to the statistical distribution of the dataset. A final semantic filtering phase identifies rules with high coherence between antecedent and consequent. Experimental results show that DERAR reduces rules by up to 85%, improving interpretability and coherence. It outperforms Apriori, FP-Growth, and H-Apriori in rule quality and computational efficiency. DERAR consistently achieves lower execution times and memory use, especially on large or sparse datasets. These results confirm the benefits of adaptive, semantically guided rule mining for generating concise, high-quality, and actionable knowledge. Full article

The tequila industry faces several environmental challenges due to its high yields of contaminants, especially tequila distillation stillage or tequila vinasses, with ten to twelve liters produced per liter of tequila. All treatments aim to shorten retention times to avoid the need for large equipment or new facilities and the saturation of residues within tequila distilleries. The complexity of tequila vinasses has led to treatments with several stages, whereby most of the organic matter content is reduced, but the treatment range results are insufficient. This study aimed to evaluate a fourth-stage tequila vinasse treatment using an electrocoagulation system that uses inexpensive electrodes (SS cathodes and iron anodes), has a low electrical consumption, and applies low voltages in order to meet safety, economic, and environmental criteria so as to comply with Mexican norm NOM-001-SEMARNAT-2021. Three sets of voltage–amperage controllable power source, a 4 mm cylindrical 304 stainless-steel cathode, and a 9 mm iron anode with 200 mL samples in 250 mL beakers were used; three replicas (R1, R2, and R3) underwent 2 h treatment at 1–6 volts to evaluate the voltage effect and 1–6 h of 5-volt treatment to assess the time effect. All samples were filtered with 8 μm and 0.25 μm meshes. Chemical oxygen demand, pH, electrical conductivity, turbidity, and color measurements (SAC for λ 436, 525, and 620 nm) were taken. The experiments determined the optimal voltage and time, considering a hydraulic retention time below 6 h. The results show that electrocoagulation of pretreated tequila vinasses effectively helps in the final removal of organic matter measured as COD, reaching values below 150 COD mg/L at 5–6 h with 5 V treatments and color reduction with 5 V, 1 h treatment. This leads to final polishing that complies with the Mexican wastewater discharge norm criteria. Full article

In order to meet the application requirements for high-precision and low-noise accelerometers in micro-vibration measurement and navigation fields, this paper presents the design and testing of an ultra-high-capacitance resolution capacitive readout circuit with attofarad-level precision. First, a differential charge amplifier circuit is employed for the first stage of capacitance detection. To suppress noise interference in the circuit, a frequency-domain modulation technique is utilized to mitigate low-frequency noise. Subsequently, a differential subtraction circuit is implemented to reduce common-mode noise. Additionally, an improved filtering circuit is designed to suppress noise interference in the final stage. The test results indicate that the designed circuit operates at a carrier frequency of 1 MHz, achieving a capacitance resolution of up to 0.103 aF/H${\mathrm{z}}^{1/2}$ and a noise floor of 25.6 $\mathsf{\mu }$g/H${\mathrm{z}}^{1/2}$, thereby meeting the requirements for high-precision and low-noise capacitance detection in MEMS accelerometers. Full article