Search Results (8,662)

With the large-scale integration of high-voltage direct-current (HVDC) transmission systems, the receiving-end AC grid exhibits weak voltage-support capability. Consequently, grid-forming (GFM) battery energy storage converters are required to satisfy both current-limiting and voltage-support requirements during fault conditions. To address the challenge of maintaining GFM characteristics while limiting overcurrent, this paper proposes an improved low-voltage ride-through (LVRT) control strategy based on adaptive virtual impedance. The inherent limitations of switching-based strategies, which may cause overcurrent and instability due to control delays, are analyzed. The feasible region of the adaptive virtual impedance is determined, and a closed-loop regulation scheme based on real-time current-amplitude feedback is developed. To verify the feasibility and effectiveness of the proposed control strategy, hardware-in-the-loop (HIL) experiments are conducted, in which the proposed strategy is compared with existing current-limiting strategies. The results demonstrate that the proposed strategy limits both transient and steady-state fault currents to approximately 1.5 pu while maintaining high converter capacity utilization, with a reactive-power increment of approximately 0.8 pu. Furthermore, under different three-phase voltage sag depths and asymmetric fault conditions, the proposed strategy effectively limits fault currents while maintaining reactive power support, thereby enhancing the transient voltage-support capability of the HVDC receiving-end AC grid. Full article

Traditional grid-forming converter (GFC) control faces fundamental challenges in maintaining DC bus stability during rapid power transients, primarily due to the limited dynamic response capability of source-side energy storage devices. To address this, this paper proposes a hybrid control strategy integrating long short-term memory (LSTM) networks with a joint GFC and storage converter (SC) control scheme. The LSTM detects short-term voltage trends from historical DC bus data to generate a feedforward compensation signal, while the joint SC-GFC control dynamically incorporates the GFC’s inertial power demand into the SC’s power reference. Hardware-in-the-loop experiments show that, compared to traditional independent control under the same step transient conditions, the proposed method can reduce power overshoot by approximately 79.2%. The LSTM-enhanced joint control maintains stable power flow and significantly suppresses low-frequency oscillations, validating the necessity of data-driven trend prediction for achieving superior inertial support in practical constrained environments. This work provides a communication-free, practical solution for enhancing GFC performance. Full article

Active-power ceilings create a control challenge in droop-controlled grid-forming converter networks because the electrical response is faster than the measurements and outer control. Projected and projection-free power limiting use filtered active power in the outer power–frequency channel and therefore cannot act directly on the first electrical power peak. This paper proposes constraint-activated projection-free control, which coordinates a shaped projection-free multiplier with a bounded resistance term in the capacitor-voltage reference driven by instantaneous terminal power. A general full-order dynamic model describes the converters, controllers, and network without tying the formulation to a particular benchmark. Local well-posedness is established, and the proposed controller is shown to preserve the constrained projection-free equilibrium and active-branch Jacobian, allowing the same full-order stability assessment. Across ten tested scenarios with unchanged controller parameters, the proposed controller reduces peak power exceedance by 38.7–55.4% and accumulated excess energy by 34.1–62.2%. The corresponding DC-buffer requirement decreases without activating the independent current limiter, which isolates the source-side power constraint from AC overcurrent. A network-level study demonstrates sequential transitions between one and two constrained sources while the remaining converter supplies the feasible power imbalance. Full-order stability verification, component studies, and parameter sweeps establish the role and useful range of each controller path. Full article

Reliable spatial evidence can support quality assurance and quality control for land use, land-use change and forestry (LULUCF), but land-cover and species-distribution layers do not by themselves identify IPCC Forest Land or species-pure stands. We estimated 2010 and 2020 above- and below-ground living biomass carbon within tree-covered European beech (Fagus sylvatica L.) distribution and occurrence footprints in Greece. Our operational hypothesis was that increasingly restrictive species masks would materially alter the mapped extent and carbon estimates. ESA Climate Change Initiative Biomass v6, ESA WorldCover 2021, European Forest Genetic Resources Programme (EUFORGEN) polygons, and Forest Information System for Europe (FISE) relative probability of presence layers were processed in Google Earth Engine. Biomass was converted with IPCC default carbon fractions and root:shoot ratios, and the results were summarized nationally and for GAUL Level-2 units. The broad EUFORGEN footprint covered 22,133 km², whereas the Combined overlap of EUFORGEN, FISE relative probability of presence ≥ 0.50, and tree cover covered 2742 km². Within the Combined footprint, the pixel mean living biomass carbon density was 60.33 Mg C ha⁻¹ in 2010 and 62.50 Mg C ha⁻¹ in 2020, and the area-integrated change was +0.58 Tg C; the area-normalized regional change was positive in 13 of 17 units and negative in 4. Across masks, the mean decadal change ranged from −0.50 to +3.37 Mg C ha⁻¹ and the approximate area-integrated totals from −1.10 to +0.58 Tg C. These scenario-conditioned estimates are neither official national greenhouse gas inventory estimates nor tests of statistical significance; instead, they provide reproducible spatial screening while making mask sensitivity and unpropagated uncertainty explicit. Full article

This article presents a review and comparative analysis of converter architectures for electrolyzer systems, covering alternating current (AC) -grid-connected, direct-current (DC) -grid-connected, and renewable-energy-connected systems, as well as isolated and non-isolated configurations. The study classifies and compares key converter topologies based on engineering criteria, including voltage gain, efficiency, device count, control complexity, and implementation feasibility. Furthermore, the relationships among converter structures, power-source characteristics, and electrolyzer-system requirements are analyzed to reveal system-level engineering trade-offs. The analysis demonstrates that converter suitability depends on the combined requirements of the power source, galvanic isolation, electrolyzer characteristics, operating conditions, and application-specific engineering priorities. In addition, wide-bandgap semiconductor devices and electrolyzer operating characteristics are discussed as important factors in converter selection, particularly for improving converter efficiency, reducing current ripple, increasing power density, and supporting dynamic operation. This article therefore provides a systematic framework for converter classification and selection according to power-source characteristics, electrolyzer requirements, and application power levels. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

Grid-forming converters are pivotal for stability support in modern power systems, where their interactive behavior is significantly determined by control parameters. However, prevalent impedance-based analysis is confined to individual control schemes, lacking a unified basis for comparative assessment and generalized parameter impact analysis. To bridge this gap, this paper develops a generalized sequence impedance model that consolidates major multi-loop GFM control strategies, structurally mapping each control loop to specific impedance components. Utilizing this generalized representation, the interactive effects of inner-loop parameters are analytically disentangled. Based on the found effects, a general parameter-tuning rule is proposed to improve the interactive stability of the GFM converter connected to different grids. Experimental validation confirms the model’s accuracy and demonstrates its utility for the systematic, stability-oriented design of GFM converters under diverse grid conditions. Full article

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain. Full article

To address large fluctuations in fertilizer flow and the difficulty of real-time quantitative blockage monitoring during fertilization by an integrated tillage, land preparation, and seeding machine, a fertilization monitoring system combining real-time detection and intelligent alarm functions was designed and developed. The system uses an STC32G12K128 microcontroller as the core control unit and integrates fiber-optic sensors, fiber-optic amplifiers, and associated peripheral hardware. Supporting host computer software was also developed on the Python3.13 platform. Based on the light-blocking principle, the optical signal generated by fertilizer particles passing through the sensing area is converted into a digital signal by the fiber-optic amplifier. A quantitative correlation model between the amount of blocked light and the fertilizer discharge rate was then established, enabling indirect and non-contact measurement of the fertilizer discharge rate. Indoor bench tests demonstrated a highly significant positive linear correlation between the amount of blocked light and the fertilizer discharge rate. The overall mean absolute percentage error of the fitted model was below 10%. Based on this detection system, a fertilization monitoring and alarm module was further developed for indoor bench conditions, together with discrimination logic for fertilizer blockage and fertilizer shortage. At rotational speeds of 30–50 r/min, the system achieved an average blockage detection rate of 98%, an average false alarm rate of 4.4%, and an alarm response time of no more than 3 s. Full article

Renewable power plants connected to low-system-strength grids are increasingly dominated by inverter-based resources (IBRs). Their point of common coupling (PCC) voltage is therefore shaped not only by reactive-power support but also by active-power ramps, network impedance, short-circuit capacity, and converter limits. Conventional Q-V droop control, fixed power-factor control, Volt/VAR control, and fixed active-power/reactive-power (P/Q) decoupling schemes often absorb active-power excursions into the voltage error, which can drive excessive reactive-power injection during fault clearing, post-fault power recovery, and phase-angle disturbances. Here, an active-power–voltage-coupling-aware reactive-power compensation (APVQ-RC) method is proposed for plant-level voltage control. The method estimates local P-V and Q-V voltage sensitivities online, reconstructs an effective voltage error, and produces a capacity-constrained reactive-power reference through smooth coupling activation. The reduced-order evaluation includes estimator conditioning, excitation screening, sensitivity-estimation error and empirical 95% estimator-error intervals, sensitivity to the smoothing factor and window length, measurement noise, converter capability saturation, and computational timing. Under P-V-coupled transients, APVQ-RC reduces voltage overshoot and reactive-power compensation energy while retaining Q-V-like support during voltage-sag-dominated events. Compared with the best scanned fixed P/Q baseline, it reduces overshoot, reactive-power compensation energy, and reactive-power peak by 42.03%, 60.35%, and 8.90%, respectively; the representative single-step calculation time is 0.0188 ms within a 1 ms control cycle. These results indicate millisecond-scale plant-level feasibility within the reduced model, while electromagnetic-transient, hardware-in-the-loop, and field validation remain necessary before deployment. Full article

Purpose: Excessive drill advancement after cortical breakthrough is a potential safety concern in orthopaedic procedures. We developed a data-driven approach to estimate the drill-tip position relative to the far cortex prior to breakthrough using time-series thrust force and spindle torque signals. Methods: Drilling experiments were performed on 268 porcine cortical bone specimens at a constant feed rate of 0.5 mm/s. A long short-term memory network was trained to estimate the drill-tip position from filtered force and torque signals. The reference position was derived from breakthrough timing confirmed by high-speed imaging and the programmed feed rate. Performance was evaluated using mean absolute error within the −2 to +2 mm peri-breakthrough interval. Two post hoc analyses examined whether model performance exceeded an elapsed-time baseline and whether pre-breakthrough force patterns were more consistent when expressed relative to breakthrough position than to drilling onset time. Results: The combined-input LSTM achieved an MAE of 0.20 mm, compared with 0.23 mm for force alone and 0.24 mm for torque alone. Among the representative architectures evaluated, LSTM showed the lowest regression error. A signal-blind time-only baseline yielded an MAE of 0.54 mm. The association between cortical thickness and force-decline onset was weaker when expressed in spatial coordinates relative to breakthrough than when expressed as time from drilling onset (R² = 23% vs. 74%). These findings suggest that force and torque signals contained information associated with proximity to breakthrough beyond that provided by average drilling duration alone. Conclusion: Converting sensor-derived resistance patterns into spatially anchored positional information may support proactive strategies such as controlled deceleration before penetration. The proposed approach represents a step toward exemplifying the emerging concept of surgeon-assisting Physical AI. Full article

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article

Modern energy systems, including those supporting transportation electrification, are increasingly exposed to volatile market conditions and external events. Effective decision-making therefore requires the integration of structured operational data with unstructured contextual information. Existing studies on Large Language Model (LLM)-assisted energy systems have mainly applied LLMs to individual tasks such as forecasting, scheduling, or decision support, while forecasting and control are typically treated separately. As a result, semantic information extracted from external events is not consistently propagated from market prediction to operational decision-making. This paper proposes a unified dual-stage framework in which the LLM functions as a shared semantic information processor, converting raw event data into structured representations used by both forecasting and control modules. In the forecasting stage, these representations improve price prediction under non-stationary conditions. In the control stage, the same information provides an event-aware contextual action prior for reinforcement learning-based energy management. This design allows external event information to inform both future-state estimation and subsequent control decisions, establishing a consistent connection between prediction and decision-making. The framework is evaluated using real-world electricity market data and a battery energy management environment. The results show that the proposed framework achieves the highest average cumulative reward among the evaluated methods while maintaining greater robustness than the forecasting-only LLM configuration. Overall, this work demonstrates the benefit of consistently propagating structured semantic information across forecasting and control and provides a viable approach to event-aware intelligent energy management, with potential extensions to multi-energy transportation systems and electrified mobility applications. Full article