Search Results (321)

The integration of grid-forming energy storage systems (GFM-ESSs) provides essential support for the stable operation of grid-connected converters in renewable energy systems. However, GFM-ESSs may exhibit low-frequency oscillations in response to grid state variations, posing a threat to power system stability. To address this challenge, this paper proposes a fast continuous optimization method for the active power–frequency control loop of multi-VSG-based GFM-ESSs. First, a parameter coupling model for multiple VSGs is established, and an internal parameter decoupling control strategy is proposed. Subsequently, an iterative optimization model based on a gradient-based master–slave game is developed, in which the minimization of converter frequency deviation serves as the leader’s objective, while the minimization of system frequency deviation acts as the follower’s objective. Frequency fluctuations are further mitigated through tracking differentiator-based active power compensation. The effectiveness of the proposed method is validated through simulation with six GFM-ESS units integrated into a modified IEEE 33-node system featuring six renewable energy stations. Simulation results demonstrate that the proposed approach significantly suppresses frequency fluctuations while also reducing the response time and the rate of frequency change under grid disturbance conditions. Full article

Many of life’s biggest dilemmas can be summed up as a tension between holding on and letting go. The very language evokes a notion of intentionality which, for the most part, has evaded scientific understanding. How might we even get a window into it? Important insights have come from a seemingly simple task: wiggling one’s fingers to and fro to the beat of a metronome. As the metronome pace increases to some critical frequency, one coordinative pattern becomes unstable and switches spontaneously to another. Such transitions are typically preceded by critical fluctuations, a predicted feature of self-organization in complex, dynamical systems. Here we address the nature and source of these fluctuations, usually assumed to be: (1) random; (2) of external origin; and (3) of fixed magnitude. We performed an experiment in which participants were instructed to oscillate their fingers in either an in-phase or anti-phase pattern in time with a metronome and instructed them to either “hold-on” or “let-go” should they feel the pattern begin to change, yielding a 2 by 2 within-subjects design. We observed that as the metronome frequency was increased from 1.00 to 3.00 Hz, fluctuations in the relative phase between the fingers were significantly altered both by the starting coordinative pattern as well as the participant’s intention to “hold it on” or “let it go”. Specifically, the intention to hold on to the anti-phase pattern delayed the spontaneous transition to in-phase, an effect that was paired with increased fluctuations beyond the critical frequency. These observations were analyzed under the extended Haken–Kelso–Bunz (HKB) model which describes the non-linear stochastic dynamics of the order parameter (relative phase) as a gradient descent on a certain potential. Our analysis, in line with experimental results, suggests that intention transforms the HKB potential not only by stabilizing unstable coordination states but also (paradoxically) by increasing fluctuations around them. Such findings may offer new interpretative light on the relation between intention and fluctuations in the coordination dynamics of living things. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving point-prediction accuracy while overlooking effective multivariate dependency modeling and reliable uncertainty quantification, limiting both the informativeness and reliability of their forecasts. This study proposes a Fractional-order Momentum optimized Evidential iTransformer (FoM-EiT) for short-term wind power forecasting from multivariate time series. The proposed model integrates cyclic feature encoding for periodic variables, an inverted Transformer for variable-wise interaction learning, and an evidential output head that jointly produces point forecasts and uncertainty estimates from a shared representation. The proposed fractional-order momentum (FoM) optimization accumulates gradient history over an extended window, thereby smoothing oscillations caused by gradient competition and stabilizing the joint training process. Experiments on four real-world wind farms from different geographical regions show that FoM-EiT achieves competitive point forecasting performance, with $R^2$ values of 0.6342, 0.8211, 0.7844, and 0.9161, and the Wilcoxon signed-rank test indicates that its advantages over the baselines are statistically significant in the vast majority of comparisons. For uncertainty quantification, FoM-EiT achieves Prediction Interval Coverage Probability (PICP) values of 0.9492, 0.9682, 0.9709, and 0.9498, while the Winkler score results further show that its prediction intervals outperform the conformal prediction and quantile regression baselines in terms of overall interval quality. These results indicate that FoM-EiT provides both accurate forecasts and trustworthy uncertainty information, making it a practical tool for dispatch, reserve allocation, and risk-aware short-term power system operation. Full article

Thermoelectric generator (TEG)-based energy harvesting (EH) has emerged as a promising solution for powering ultra-low-power electronic systems. However, the inherently low output voltage of miniature TEGs is often below a range of 40–100 mV under small temperature gradients, presenting a fundamental cold-start challenge for DC-DC boost converters, preventing fully autonomous operation without dedicated start-up circuitry. Although numerous start-up techniques have been reported, the existing literature lacks a focused, design-oriented review of circuit architecture specifically optimized for ultra-low-voltage TEG applications. This paper addresses this gap by introducing a unified classification framework and providing a structured, topology-oriented analysis of state-of-the-art start-up strategies for TEG-based EH systems. Reported techniques are organized into five categories: external energy assistance, mechanical switch-assisted techniques, multi-source EH, transformer-based architectures, and oscillator-driven DC-AC-DC conversion. Each category is comparatively evaluated in terms of start-up voltage, integration level, efficiency, and system autonomy. Among these, oscillator-based approaches, particularly ring oscillator (RO) architectures, emerge as the most viable pathway toward fully integrated and scalable implementations, owing to their CMOS compatibility and architectural flexibility. The review further discusses key design trade-offs, handover stability challenges, and practical limitations, and provides architectural insights to guide the development of next-generation autonomous TEG-powered platforms. Full article

An analytical solution is presented for transient heat conduction in a two-layer composite cylinder subjected to outer-surface convection with a general time-dependent ambient temperature. Using Duhamel’s principle, closed-form series expressions are derived and then specialized to harmonic ambient fluctuations, recovering the classical constant-ambient solution in the zero-frequency limit. A parametric study shows that the ratio of the inner layer conductivity to the conductivity of the outer layer strongly shapes interfacial gradients and mean-temperature evolution, with sensitivity concentrated at small ratios and diminishing when the ratio is larger than 0.1. Increasing Biot number accelerates the heat transfer and approaches the isothermal-surface limit as it becomes extremely large. The geometric aspect ratio is most influential when the inner layer is resistive, and becomes weak for large conductivity ratio, supporting thin-coating approximations. Under harmonic ambient fluctuations, the response rapidly reaches a periodic steady state; higher frequency decreases amplitude and increases phase lag, while larger Biot numbers amplify oscillations and reduce delay. The coupled effects of the aspect ratio and the conductivity ratio govern penetration and phase behavior. Full article

The role of unmanned aerial vehicles (UAVs) in time-sensitive missions such as low-altitude reconnaissance and disaster rescue has gained increasing significance. To address the challenge of visual localisation for UAVs operating in complex terrains under Global Navigation Satellite System (GNSS)-denied environments, this paper proposes an improved adaptive gradient-damped Newton approach to mitigate the trade-off between terrain non-convexity and computational real-time performance. The proposed approach incorporates a terrain-gradient-based dynamic step-size adjustment mechanism that adaptively captures non-linear terrain characteristics in real time and effectively reduces the numerical oscillations typically observed in steep regions when using the standard Newton method. In addition, a tightly coupled vision–geometry framework was developed to constrain cumulative drift during long-range flight. Monte Carlo simulation results demonstrate that the proposed algorithm maintains submeter localisation accuracy while achieving approximately a three-fold improvement in computational efficiency compared with traditional grid-based methods, and a 27.4% increase in convergence speed relative to the standard Newton method. Experiments conducted under high-noise conditions and highly undulating terrains indicate that the approach exhibits strong convergence stability, offering a computationally efficient and robust solution for UAV navigation. Full article

We consider Riemann soliton vector fields and Ricci bi-conformal vector fields on the oscillator group. We prove that the oscillator group admits Riemann solitons. Subsequently, we provide a complete classification of all Ricci bi-conformal vector fields admitted by the oscillator group and identify those that belong to specific categories, namely gradient-type vector fields, Killing vector fields, and Ricci collineation using the partial differential equations. Full article

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for understanding and anticipating global climate variability. Although deep learning (DL)-based models have recently improved ENSO forecasting skill, achieving strong predictive performance while maintaining model interpretability remains a major challenge. Existing approaches may suffer from instability or have difficulty distinguishing contributions across multiple variables and time steps. To address this issue, this study presents an interpretable ENSO forecasting framework that combines a ConvNeXt-based deep learning model, ENSO-ConvNeXt, with an improved gradient-based contribution analysis method whose calculation strategy is adjusted according to different ENSO phases. The simplified ConvNeXt architecture facilitates the integration of interpretability methods while retaining strong predictive capability. ENSO-ConvNeXt achieves competitive forecasting skill with an effective lead time exceeding 20 months, accurately capturing the Niño3.4 index evolution during the peak season and the temporal evolution of ENSO events. The case studies of representative ENSO events demonstrate that the major contribution regions identified by the model are broadly consistent with established ENSO variability patterns across major ocean basins. These results highlight the potential of our framework to advance ENSO prediction while providing statistically grounded and physically interpretable insights. Full article

To address the uncertainty of renewable energy output power and load demand in the park-level multi-source synergistic power supply system and achieve economical system operation, an optimal scheduling method based on the mathematical programming guided imitation learning-oscillation-decaying deep deterministic policy gradient (MPG-OD-DDPG) algorithm is proposed. First, a scheduling model of the park-level multi-source synergistic power supply system is established with the objective of minimizing system operating cost. Second, to address the low sample utilization efficiency of the deep deterministic policy gradient (DDPG), a mathematical programming guided imitation learning (MPGIL) method is introduced to obtain demonstration experience and guide the initial training process. Then, targeting insufficient exploration capability of DDPG, an oscillation-decaying Ornstein–Uhlenbeck noise (OD-OU) is designed to enhance the exploratory capability of the algorithm through the oscillation-decaying process of noise intensity. Finally, according to the scheduling model, the reward function is designed, and the proposed algorithm is used to search for the optimal energy regulation strategy, thereby realizing the optimal scheduling of the park-level multi-source synergistic power supply system. Case study results show that compared with the DDPG, the MPG-OD-DDPG reduces the operating cost by 7.41%. It can effectively exploit the energy time-shift capability of the energy storage system (ESS), reduce the operating cost of the park-level multi-source synergistic power supply system. Full article

Environmental loading is a major driver of nonlinear GNSS vertical displacements, yet its spatiotemporal heterogeneity remains insufficiently understood in regions with complex topography. In this study, we investigate the environmental loading effects on GNSS vertical motions across Southwest China using observations from a network of 66 stations. Singular Spectrum Analysis (SSA) and Empirical Orthogonal Function (EOF) analysis were applied to extract annual signals, while component-wise RMS reduction quantified hydrological and atmospheric loading contributions. Spatial statistical analysis, cross-wavelet transform, and k-means clustering examined correlation patterns and phase hysteresis between GNSS observations and modeled loads. Results show that hydrological loading dominates seasonal vertical oscillations, but crustal responses exhibit pronounced spatial heterogeneity controlled by regional topography and hydro-climatic gradients. EOF analysis reveals a dipole pattern induced by the Hengduan Mountains’moisture-blocking effect. Atmospheric loading anomalously dominates the eastern Sichuan Basin, whereas Yunnan displays strong amplitudes with high heterogeneity due to karst hydrogeology. Phase analysis identifies three distinct regimes: a rapid elastic response on the Tibetan Plateau, (with the lag of ~20 ± 5 days, correlation coefficient R ≈ 0.65), intermediate delays in Yunnan (~60 ± 5 days, R ≈ 0.58), and pronounced hysteresis in the Sichuan Basin (~105 ± 5 days, R ≈ 0.38) linked to slow groundwater diffusion and poroelastic processes. These findings highlight the critical role of local hydrogeological dynamics in modulating GNSS vertical deformation and provide new insights for improving environmental loading corrections in complex mountainous regions. Full article

Sandy shorelines respond to variability in boundary conditions over a wide range of time and spatial scales. While recent studies show that climate modes may affect shoreline evolution at interannual scales, such relationships remain unclear in the South Atlantic Ocean. Here, we investigate whether climate mode-driven variability in wave climate influences shoreline evolution using Ilha Comprida, a barrier island on the southeastern Brazilian coast, as a case study. Offshore wave conditions from the ERA5 reanalysis were analyzed over the last four decades and propagated to the nearshore using wave modeling. Shoreline change was quantified from satellite-derived shoreline positions, and relationships with interannual climate modes were evaluated using climate indices. Results show that the wave climate is bimodal and dominated by swell, with strong seasonality and no significant long-term trend in storminess. The El Niño–Southern Oscillation (ENSO) influences wave energy and extremes, with La Niña phases associated with higher wave power without a change in wave direction. No significant signal of the Southern Annular Mode (SAM) was found. At the coast, shoreline evolution is controlled by long-term sediment redistribution driven by alongshore transport gradients. ENSO-related shoreline signals are weak and spatially limited, occurring only in lower Empirical Orthogonal Function (EOF) modes of variability. These results suggest that, at Ilha Comprida, ENSO mainly modulates episodic wave-driven events rather than long-term shoreline patterns, emphasizing the need to distinguish between short-term energetic variability and longer-term morphodynamic response. This distinction is important for coastal management because even where climate modes do not produce persistent long-term shoreline trends due to site-specific aspects, they may still modulate event-scale risk, which can vary independently of the long-term average shoreline behavior. Full article

The significant interannual variability in Madden–Julian Oscillation (MJO) intensity remains incompletely understood. Empirical orthogonal function (EOF) analysis reveals that the first three leading EOF modes of the annual mean MJO intensity are significantly correlated with the Quasi-Biennial Oscillation (QBO), Eastern Pacific El Niño-Southern Oscillation (ENSO), and Central Pacific ENSO. Focusing on the distinct EOFs related to three key tropical interannual variabilities, we conduct an investigation into the potential governing processes through which the changes in background mean states impact MJO intensity based on the MJO moisture mode theory. Observations suggest that the accumulation of moist static energy (MSE) during MJO moistening phases and its dissipation during drying phases play a crucial role in regulating MJO amplitude. At the interannual timescale, regions characterized by positive EOF values display positive (negative) MSE tendency anomalies during MJO moistening (drying) phases, leading to amplified MSE accumulation (dissipation) throughout the MJO lifecycle and subsequently facilitating an increase in MJO amplitude. Conversely, regions with negative EOF values exhibit opposing trends. Further analysis reveals that these MSE tendency anomalies are mainly associated with the zonal advection term, which is influenced by interannual changes in the background mean MSE and low-level winds. The spatial pattern of the background mean MSE is strongly linked to sea surface temperature (SST) anomalies, with low-level background winds aligning well with the horizontal gradients of SST anomalies. Full article

Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. As a result, thermal instability becomes a recurrent operational bottleneck in biogas plants without active temperature control. This review examines the thermal and dynamic behavior of anaerobic reactors from a process-engineering perspective. It integrates energy balances, heat-transfer mechanisms, and computational fluid dynamics (CFD) modeling. The combined effects of temperature gradients, hydrodynamic mixing patterns, and structural material properties are analyzed to determine their influence on thermal homogeneity, microbial stability, and methane yield consistency under mesophilic conditions. Technological strategies to mitigate thermal losses are evaluated. These include passive insulation using low-conductivity materials, geometry optimization supported by numerical modeling, and thermal recirculation schemes, as these factors govern temperature distribution and process resilience. Current limitations are also discussed, particularly the frequent decoupling between ADM1-based kinetic models and transient heat-transfer analysis. This separation restricts predictive capability under real-scale diurnal temperature oscillations. The development and validation of coupled hydrodynamic–thermal–biokinetic models under fluctuating neotropical boundary conditions are proposed as critical steps. Such integrated approaches can enhance operational stability, ensure consistent methane production, and improve energy self-sufficiency in organic waste valorization systems. Full article

Multi-robot systems (MRS) have attracted a lot of attention from researchers due to their widespread application in various environments. However, in multi-robot collaborative search tasks, two problems often arise: sparse rewards for capturing targets and control oscillations. To address these issues, this paper proposes the hierarchical active perception multi-agent deep deterministic policy gradient (HAP-MADDPG) framework. This framework guides robots to efficiently explore maps and discover targets through global utility planning based on global exploration rate and local information aggregation based on local exploration rate. A stability control mechanism, which includes hysteresis logic and reward decay, is introduced to suppress control oscillations. Experimental results show that the HAP-MADDPG framework achieves a success rate of 96.25% and an average search time of 216.3 steps. The path trajectories are smooth, demonstrating the effectiveness of the proposed approach. Full article