Search Results (148)

Reliable discrimination between power swings and short-circuit faults is essential for secure transmission line protection, since misclassification may lead to unnecessary tripping or delayed fault clearing. Conventional power swing blocking techniques, particularly those based on impedance trajectory analysis, often require system-dependent settings and may exhibit reduced reliability under dynamic operating conditions with increasing renewable generation penetration. This paper proposes a setting-independent method for power swing and fault discrimination based on the Second Central Moment (SCM) of normalized instantaneous voltage and current signals. In this context, setting-independent means that the method does not require line-specific protection settings or case-by-case threshold tuning, although nominal voltage, nominal current, system frequency, and sampling frequency are required for signal normalization and sliding-window implementation. The SCM provides a statistical measure of signal dispersion that enables classification into three operating states: steady-state operation, power swing conditions, and fault events. Common SCM decision boundaries are applied without adjustment across the evaluated transmission lines, operating conditions, fault characteristics, power-swing frequencies, and levels of inverter-based resource penetration. The proposed method is validated through time-domain simulations using the Kundur two-area benchmark system and the IEEE 14-bus network under a wide range of disturbance scenarios, including oscillatory conditions, symmetrical and asymmetrical faults, renewable integration, and swing–fault sequences. For benchmarking purposes, the SCM-based algorithm is compared with a commercial Swing Center Voltage (SCV)-based blocking scheme widely implemented in digital relays. The results show that the proposed method achieves reliable swing–fault discrimination with low computational complexity while providing earlier blocking activation under slow oscillatory conditions and inherent fault discrimination capability. These characteristics support its practical application in real-time transmission line protection. Full article

The direct stator connection of a doubly fed induction generator (DFIG) makes it highly sensitive to grid-voltage disturbances. Although a supercapacitor-supported dynamic voltage restorer (SC-DVR) can restore the machine-terminal voltage, assigning the entire fault-induced power imbalance to the supercapacitor increases the required storage capacity. To address this problem, an adaptive coordinated fault ride-through control strategy is proposed for a DFIG–SC-DVR wind–storage system. A unified dynamic model is first established to describe the electromagnetic response, dc-link energy imbalance, rotor kinetic-energy exchange, and series voltage compensation under symmetrical and asymmetrical voltage sags. The remaining adjustable kinetic-energy margin of the rotor and the remaining adjustable energy margin of the supercapacitor are then used to construct an online coordination coefficient. Accordingly, the active-power references of the DFIG and SC-DVR are dynamically adjusted to match their instantaneous regulation capabilities. Time-domain simulations show that the proposed strategy maintains the machine-terminal voltage and keeps the rotor current, dc-link voltage, and rotor speed within prescribed limits. Compared with non-coordinated control, it reduces the SC-DVR absorbed energy by 51.43% and 57.72% under symmetrical and asymmetrical faults, respectively; compared with fixed-ratio coordination, the corresponding reductions are 34.69% and 29.81%. Supplementary sweeps over wind speeds of 10–12 m/s and short-circuit ratios of 2–10 further verify the bounded applicability of the method without retuning the controller. 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

To overcome the limitations of insufficient driving force in traditional microbial desalination batteries, this paper constructs a microbial anode-membrane capacitive deionization (B-MCDI) coupling system. For the first time, direct coupling between extracellular electron transfer in Shewanella oneidensis and double-layer adsorption at the MCDI cathode is achieved at the circuit and material levels, realizing self-driven, low-energy desalination. High-specific-surface-area carbon felt is used as the anode, and a stable electrogenic biomembrane (output voltage >400 mV) is formed after directional domestication with Shewanella oneidensis MR-1. Activated carbon is used as the cathode to construct the MCDI electrode. In the three-chamber reactor, the desalination chambers are separated by cation and anion exchange membranes. Under the drive of the bioelectric field, Na⁺ and Cl⁻ selectively permeate into the cathode and anode chambers, respectively, effectively suppressing the co-ion effect. Under optimal operating conditions (external resistance 1000 Ω, initial NaCl concentration 2.0 g/L), the system achieved a cumulative desalination rate of 85.1% after 12 h of operation, with a salt adsorption capacity of 162.1 mg/g, an average desalination rate of 13.51 mg/(g·h), and an energy consumption of only 0.58 kWh/m³. This demonstrates that bioelectric energy can effectively provide targeted power to drive capacitive adsorption and desalination. Under initial NaCl concentrations of 1.0 g/L and 3.0 g/L, the highest desalination rates reached 78% and 68%, respectively. The maximum instantaneous desalination rate occurred within 0.5–1.0 h (64 mg/h under 2.0 g/L conditions), exhibiting a three-stage kinetic characteristic of “fast-slow-equilibrium”. The energy consumption in this study was only 0.51 kWh/m³, further demonstrating the high energy efficiency of bioelectrically coupled MCDI in low-salinity treatment areas. Therefore, this B-MCDI can serve as a theoretically feasible proof-of-concept technology for desalination of brackish water that meets the requirements of self-driven, low-energy consumption, and has promising applications in decentralized water supply systems in areas with limited energy supply or no available electricity. Full article

Reliable photovoltaic (PV) operation requires monitoring strategies that can detect performance degradation before it develops into persistent efficiency loss. This study proposes an interpretable data-driven framework for condition monitoring and early warning of low-efficiency events using only inverter-based electrical measurements. The novelty of this study lies in its focus on detecting low-efficiency operating conditions from inverter electrical data, rather than merely classifying individual PV fault types. Thirty-minute operational data from a 110 kW grid-connected PV plant in Kastamonu, Türkiye, covering January 2023–December 2025, were analyzed. Phase currents, phase voltages, total active power, and DC power were transformed into electrical health indicators, including mean current, mean voltage, current and voltage variability, phase imbalance index, and conversion efficiency. Correlation and imbalance analyses showed highly synchronized three-phase operation, with a mean phase imbalance index of 0.004865. Conversion efficiency remained stable, with an instantaneous mean of 0.964. Generalized Additive Model results explained 66.4% of efficiency variability and identified mean current as the dominant nonlinear determinant, while phase imbalance acted as a secondary but significant factor. A Random Forest classifier achieved 96.34% accuracy, 3.87% out-of-bag error, and 53.4% recall for rare low-efficiency events. Decision-tree rules indicated high risk when mean current fell below 9.4 A and very low risk above 12 A. The framework provides a practical, sensor-minimal, and interpretable approach for PV performance monitoring and proactive maintenance. Full article

This paper addresses the challenge of maintaining low current stress and high efficiency in dual-active-bridge (DAB) microinverters, where the voltage conversion ratio and instantaneous transferred power vary continuously over the line-frequency cycle. A mode-switching, four-degree-of-freedom, variable-frequency modulation strategy with analytical parameter calculation is proposed. Four representative low-current-stress operating modes are selected for the $G<1$ and $G>1$ regions. Under unity-power-factor and sinusoidal grid-current-tracking constraints, analytical switching-frequency expressions are derived for each mode, enabling coordinated control of the primary-side duty ratio, secondary-side duty ratio, external phase-shift ratio, and switching frequency without iterative online optimization. Duty-ratio limits, switching-frequency bounds, ZVS commutation-current requirements, and an SPS-based hysteresis transition near $G=1$ are incorporated. A 500 W experimental prototype was built for validation. The proposed strategy achieved a peak efficiency of 97.2% at 300 W and an efficiency of 96.5% with a grid-current THD of 2.8% at 500 W. Compared with a fixed-frequency minimum-current-stress TPS strategy, the measured peak leakage-inductor current at 500 W was reduced from 8.23 A to 7.42 A. The results validate the proposed modulation method under the reported experimental conditions. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

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

Shoulder injuries due to labor-related lifting tasks are widespread in manufacturing and logistics companies. Prolonged shifts and repetitive motions lead to muscle fatigue, significantly elevating the risk of both acute accidents and chronic musculoskeletal disorders. Many passive exoskeletons which use springs to provide lifting assistance have been commercialized, and many active exoskeletons have been researched. The drawback to passive exoskeletons is the larger the lifting force that they produce, the larger the force required to lower the arms. This contributes to tiring the user. Conversely, active exoskeletons require substantial energy to provide meaningful torque. Furthermore, they pose a safety risk; a sudden power failure could result in an instantaneous loss of support, potentially causing the user to drop a heavy load and sustain injury. This research project proposes a hybrid exoskeleton with a parallel elastic actuator that uses a motorized helical actuator which can be tuned to improve lifting performance. This paper evaluates the kinematics and statics of the proposed exoskeleton, details the design and implementation of the electrical control system, shows mechanism optimization of the mechanical advantage profile, and validates the concept through the construction and experimental testing of a functional prototype. Full article

The paper presents a fast adaptive power control with implicit predictive behavior for an on-board power converter operating in support of a 400 Hz aircraft electrical network. Accurate control of active and reactive power in such high-frequency networks requires precise estimation of the network voltage phase, frequency, and amplitude. Therefore, a proposed adaptive phase-locked loop (PLL) algorithm is integrated with a proportional resonant current controller (PR). The adaptive PLL continuously estimates the instantaneous phase, frequency, and amplitude of the fundamental voltage component, enabling fast synchronization and dynamic adjustment of the PR controller resonant frequency. Consequently, the combination familiarises anticipatory response characteristics with the control loop without the need for computationally intensive model predictive control algorithms. The simulation results demonstrate that the proposed method significantly reduces the synchronization time, maintains high accuracy under frequency variations and harmonic distortion, and exhibits robustness against measurement noise. Furthermore, the modular and computationally efficient structure of the algorithm makes it suitable for real-time implementation of FPGA. The proposed approach provides an effective solution for high-performance power management in aircraft electrical systems, ensuring precise power control under hard dynamic conditions. Full article

Traditional reliability models for distribution grids often rely on static historical averages, overestimating the operational lifespan of power system assets by neglecting the dynamic interplay between electrical loading and microclimatic stressors. This paper addresses these limitations by introducing an extended analytical framework designed to integrate climate-driven stressors into traditional reliability assessments, capturing the synergistic effects of environmental forcing and asset aging. This methodology is operationalized through a novel simulation framework and a modular Python-based tool (Python version 3.10.20), integrating OpenDSS and Pandapower to perform high-fidelity reliability assessments. By calculating instantaneous failure rates and Mean Time Between Failures (MTBF) as functions of real-time environmental forcing—specifically temperature and humidity-induced stresses—the proposed system captures degradation dynamics that remain invisible to conventional models. The framework’s capabilities are demonstrated through a simulation on a rural distribution grid, which explicitly includes auxiliary digitalization components, such as Remote Terminal Units (RTUs), that are frequently overlooked in standard benchmarks. The results reveal that environmental forcing triggers a sharp contraction in the MTBF of critical active assets, proving that asset seniority alone is an insufficient proxy for grid vulnerability. Full article

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation. Full article

Given the expanding scale of offshore wind power development, strict spatial constraints on offshore platforms and multi-source power coupling present operational challenges during the collaborative transmission of near-shore and far-offshore wind power through a shared corridor. To address these issues, this paper proposes a collaborative transmission scheme based on the Self-Adaption Statcom and Line-Commutation Converter (SLCC). The technical and economic characteristics of three typical topologies—Modular Multilevel Converter (MMC) onshore grid connection, MMC direct transmission, and SLCC direct transmission—are compared and analyzed. The results demonstrate the advantages of the SLCC scheme in reducing the offshore platform footprint and lowering engineering costs. Furthermore, a hierarchical collaborative control strategy is designed to mitigate the power coupling between near-shore AC wind generation and far-offshore DC wind generation at the converter bus. The bottom layer utilizes a valve-side parallel Static Var Generator (SVG) to achieve reactive power self-balance and quasi-resonant suppression of specific harmonics. In the top layer, an LCC active power-following control strategy based on instantaneous power feedback is implemented. This achieves the logical decoupling of near-shore and far-offshore wind power transmission. The effectiveness of the proposed scheme in managing wind power fluctuations, riding through AC faults, and maintaining stable operation under weak grid conditions is verified using the PSCAD/EMTDC software. Full article

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts. Full article

When the Risley prism pair is used for target tracking, the nonlinear relationship between beam deflection and prism rotation makes tracking performance highly dependent on precise and stable motor control over a wide speed range. Although the brushless DC motor serves as the preferred drive source, its inherent commutation torque ripples directly induce beam pointing jitter, severely degrading overall tracking accuracy and stability. To address these issues, this paper proposes a three-vector-based model predictive direct speed control method. This approach establishes a direct speed-to-torque control channel by generating reference active power through dynamic equations, eliminating the need for fitting a constant flux linkage and parameter tuning. Simultaneously, combined with three-vector optimization and seven-segment modulation strategies, it achieves a dynamic balance between high-frequency, instantaneous electromagnetic power fine-tuning and inherent mechanical inertia of the rotor. Simulation results demonstrate that the proposed method exhibits superior speed stability compared to the conventional double-vector-based model predictive power control method and maintains high-precision dynamic tracking over a wide speed range. Ultimately, it leads to an average reduction of over 60% in the time-weighted absolute tracking error integral under various target trajectories, providing an effective solution for drive control of target tracking in Risley prism systems. Full article