Search Results (134)

Electrospun sulfonated polyketone (PK) nanofiber membranes were prepared to investigate the sulfonation-time-dependent structure–property relationships of hydrocarbon-based polymer electrolyte membranes for PEMFC (Polymer Electrolyte Membrane Fuel Cell) applications. NaCl addition to the electrospinning solution increased solution conductivity and enabled the formation of uniform PK nanofibers with an average diameter of approximately 270 nm. Subsequent sulfonation introduced sulfonic-acid-related groups into the PK nanofiber framework, and the resulting membrane properties were strongly governed by sulfonation time. Among the tested membranes, PK-NC16 exhibited the highest proton conductivity of 0.107 ± 0.031 S cm⁻¹ and an ion exchange capacity of 2.82 m_eq g⁻¹, exceeding or comparable to those of Nafion 115 under the tested conditions. FTIR-based analysis indicated that the relative sulfonation index increased up to 16 h, whereas extended sulfonation for 24 h generated additional sulfone/sulfonate-related bands, suggesting possible side reactions or structural changes under prolonged acid treatment. The high water uptake of PK-NC16 enhanced proton transport but also revealed a hydration-sensitive polymer network, as reflected by a voltage degradation rate of approximately −590 μV h⁻¹ during a 100 h short-term stability constant-current test. These results demonstrate that sulfonation time is a key parameter controlling the balance among ionic functionality, hydration, mechanical response, proton conductivity, and PEMFC-relevant single-cell performance in electrospun PK nanofiber membranes. Full article

This paper addresses the “dual-high” challenges posed by high proportions of renewable energy and power electronic equipment in new power systems, and investigates the active support characteristics of grid-forming energy storage stations in terms of voltage and frequency. Regarding voltage support, the paper analyzes the transient process of a three-phase short-circuit fault in the power grid and proposes a low-voltage ride-through control strategy based on the flexible adjustment of active power and voltage commands. By suppressing short-circuit currents and power-angle instability during the fault, this strategy effectively enhances the system’s transient stability. The effectiveness of this strategy when the grid voltage drops to zero was verified through PSCAD/EMTDC simulations. Regarding frequency support, a small-signal model for frequency regulation of grid-forming converters was established, revealing the influence of controller parameters on the system’s virtual inertia. Simulation results indicate that grid-forming control possesses adjustable inertial support capabilities, effectively enhancing the system’s frequency stability. This research provides a theoretical basis and control strategy support for the application of grid-forming energy storage stations in power grids with a high proportion of renewable energy. Full article

This paper addresses the operational challenges introduced by the growing share of intermittent renewable energy sources in islanded microgrids. Traditional unit commitment (UC) methods struggle to manage the continuous variations in demand and renewable generation effectively because dispatch setpoints remain fixed between scheduling intervals. To overcome these limitations, a dynamic voltage and frequency controller (DVFC) is proposed. The DVFC uses adaptive critic control and approximate dynamic programming to update mid-level control actions based on measured microgrid states, technical constraints, and look-ahead utility functions. The proposed method is applied to short-term UC, ensuring frequency and voltage regulation while maintaining microgrid stability. Simulation results on the modified CIGRE test system demonstrate that the DVFC reduces frequency deviations by up to 40–50% and voltage deviations by 60–65% compared to conventional UC. In addition, the method lowers operating costs by up to 6% and extends the effective battery lifecycle by nearly twofold by reducing stress and cycling. These results confirm that the DVFC significantly outperforms conventional UC algorithms in both technical performance and economic efficiency. Full article

When a fault occurs in the power grid to which the Virtual Synchronous Generator (VSG) is connected, it leads to overcurrent phenomena, which threatens the safety of the inverter and easily results in device damage. Although existing direct current limiting unit (CLU) control strategies can restrict the fault current, the input active power command far exceeds the power output, causing the virtual rotor to continuously accelerate. This leads to power angle divergence and a subsequent loss of synchronization. To address the conflict between direct current-limiting control and system transient stability, this paper proposes a control strategy based on the Particle Swarm Optimization (PSO) algorithm to modify the active power command, building upon existing direct current-limiting VSG control. During grid faults, the output current is constrained to its maximum value, leading to a reduction in the system’s output power. By leveraging the PSO algorithm, the proposed strategy decreases the active power command to minimize the power mismatch between the command and the output. This maximizes the system’s transient stability by minimizing the rotor acceleration torque and effectively suppressing excessive power angle deviation. Meanwhile, the active power command reduction is introduced as a penalty term to maximize the active power output capability during the fault period. Simulation results demonstrate that, compared to VSG with only direct current-limiting control, the proposed strategy significantly enhances the transient stability and transmission efficiency of the VSG under long-term fault conditions across various grid voltage sag scenarios. Furthermore, it ensures a seamless transition from the fault state to normal operation during short-term faults. Full article

Accurately estimating the state of health (SOH) of lithium-ion batteries remains challenging for battery management systems. Traditional data-driven methods, such as long short-term memory (LSTM), lack physical interpretability and often fail to generalize across varying operating conditions. To address this, a physics-informed SP-LSTM framework is proposed that integrates the single particle model (SPM) with a bidirectional LSTM network. A hybrid optimization strategy combining particle swarm optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounds (L-BFGS-B) is first used to identify key SPM parameters, which are then combined with macro external features (charging time, discharge energy, IC peak) to form a seven-dimensional fusion vector. A dual-stream Bi-LSTM architecture separately models fast-varying macro trends and slow-varying physical parameters, achieving robust SOH mapping. Validated on the NASA PCoE dataset, the proposed SP-LSTM achieves a root mean square error (RMSE) of 0.0136 and a mean absolute error (MAE) of 0.0089 on an independent test set (B0018), outperforming the baseline LSTM by 38.2% in RMSE. Noise robustness tests (0–3% voltage noise) and Sobol global sensitivity analysis further confirm its stability and interpretability. By embedding electrochemical priors into the data-driven pipeline, this work provides a practical physics-data collaborative framework for accurate and trustworthy battery SOH estimation. Full article

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

In energy conversion semiconductor devices, radiation damage is directly related to the long-term stability of β-voltaic batteries. In this study, single-crystalline silicon P⁺NN⁺ devices and P⁺-silicon materials with SiO₂ surface passivation were irradiated using a ~70 keV accelerator electron beam in a nitrogen atmosphere for 2 min, 10 min, 1 h, 6 h, and 12 h. The tritium-voltaic output decreased rapidly within the first 2 min of electron beam irradiation and then decayed slowly. After 1 h of irradiation, both the output short-circuit current (Isc) and open-circuit voltage (Voc) remained stable. The effects of the damage were analyzed using typical samples irradiated for 1 h. Neutron reflectometry (NR) was employed as the primary characterization method, while X-ray photoelectron spectroscopy (XPS)—combined with Ar⁺ etching—and secondary ion mass spectrometry (SIMS) were used to verify radiation-induced structural changes at the SiO₂ surface and SiO₂/Si interface. It was found that nitrogen atoms from the atmosphere penetrated the SiO₂ layer to a depth of approximately 5–10 nm, forming a non-stoichiometric SiON structure, without further diffusion into deeper layers. Irradiation significantly increased the thickness of the SiO₂/Si interface transition layer to about 14–18.5 nm, and the SiO₂ structure within this layer became relatively loose. It can be inferred that tritium-voltaic batteries using SiO₂-surface-passivated single-crystalline silicon P⁺NN⁺ devices as energy-conversion units and packaged in a nitrogen atmosphere can stably provide power for 10 years, with an Isc reduction of no more than 12% and a Voc reduction of no more than 6%, excluding the spontaneous decay of tritium. 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

The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert a form of primary energy into electrical energy. Primary energy comes from a number of sources, such as fossil fuels, nuclear energy, hydropower, wind, and solar. The carbon neutrality targets set by the European Union and several countries around the world have driven a transformation characterized by the gradual replacement of synchronous thermal generation based on fossil fuels with Renewable Energy Sources (RES), such as wind and solar. The energy transition, while necessary to achieve the established targets, introduces significant challenges to the stability of Electrical Power Systems (EPS) and electrical grids, since RES do not yet contribute to stability at levels comparable to the generating units of large thermal power plants, whether in terms of inertia, which has seen a notable reduction in recent years, or in voltage control or short-circuit power. This article presents and discusses solutions to mitigate the effect of this reduction in inertia in power plants using synchronous compensators and synthetic inertia emulation using battery storage. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

Digital substations (DSs) play a critical role in modern Energy and Power Electrical Systems (EPESs), enabling intelligent control, monitoring, and automation. With increased reliance on communication and sensing technologies, DSs are vulnerable to cyberattacks such as False Data Injection (FDI). An adversary may falsify transformer temperature readings, misleading protection mechanisms and resulting in incorrect disconnection actions. These false disconnections may disrupt power delivery, cause economic losses, and reduce equipment lifespan. To address these challenges, we propose a reinforcement learning-based approach for cyber protection of smart grids against false temperature data injection attacks. Specifically, this work designs a Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG) deep reinforcement learning algorithm that learns to detect normal patterns and responds to suspicious thermal patterns by dynamically adjusting disconnection decisions. The agents process sequential state features to differentiate between legitimate overload conditions and sudden anomalies caused by FDI attacks. We implement the proposed approach on the IEEE 30-bus distribution network using the Pandapower simulator. The experimental results indicate that the LSTM-DDPG controller outperforms conventional DDPG and DQN baselines, achieving a recall of 0.897, F1 of 0.945, precision of 1.00 and accuracy of 0.981 with a confidence interval of 95%. In addition, grid stability reaches up to 0.9815, 1.0, 1.0, 0.9926 with respect to the voltage stability score, transformer stability value, disconnection stability, and stability index, respectively. The proposed method led to fewer false disconnections, providing improved robustness against sensor manipulations. Full article

In this paper, a short-term active power curtailment (ST-APC) strategy for doubly-fed induction generator (DFIG) wind farms is proposed to enhance first-swing rotor angle stability under fault disturbances. While wind power is a clean renewable resource that is widely deployed, its large-scale integration heightens concerns about transient stability. After analyzing DFIG operating principles, this study advocates for using short-horizon active power control to mitigate the adverse stability impacts of wind farms. Using the Western System Coordinating Council (WSCC) three-machine nine-bus test system, the effectiveness of the ST-APC strategy across diverse operating conditions was verified. This study is based on the fundamental principle that reducing the output of wind turbines is required for first-swing stability after faults to increase the kinetic energy of synchronous machines. A closed-loop control strategy combining voltage drop, frequency change, and a timer is designed. The correlation laws between various control parameters such as control activation timing, duration, and modulation depth and first-swing stability are analyzed, providing references for parameter selection in engineering applications. The findings indicate that the proposed strategy is practical and adaptable, making it suitable for power systems with high wind power penetration. Full article

Accurate capacity prediction is paramount for ensuring the operational safety and reliability of lithium-ion battery management systems (BMS). Nevertheless, contemporary data-driven approaches often grapple with limited feature representation—frequently relying solely on aggregate charging duration or noise measures—which compromises the robustness of these approaches. To address these limitations, this study proposes a robust framework integrating multi-point voltage temporal sampling (MVTS) with an adaptive gated hybrid ensemble learning strategy. The MVTS method is first used to extract high-dimensional geometric features from the constant-current (CC) charging phase (3.9 V–4.15 V), effectively capturing subtle degradation patterns. Subsequently, an unsupervised isolation forest algorithm is incorporated for automated anomaly detection and rectification, thereby augmenting data stability prior to training. In the fusion stage, a heterogeneous hybrid model comprising eXtreme gradient boosting (XGBoost) and long short-term memory (LSTM) is constructed. An adaptive gating mechanism based on random forest (RF) is added to dynamically weight the base learners. To mitigate data leakage during the stacking process, this study employs an out-of-fold (OOF) training strategy based on leave-one-battery-out (LOBO) cross-validation to generate unbiased meta-features for the gating model. This mechanism dynamically modulates fusion weights contingent upon the multi-point voltage features and model discrepancies, thereby accommodating diverse aging stages and capacity degradation patterns. Experimental results from the NASA battery aging dataset demonstrate that the proposed framework significantly outperforms single-model baselines in terms of RMSE and ${\mathrm{R}}^2$, exhibiting superior adaptability and predictive precision. Full article

Additive engineering has become a critical strategy for optimizing the morphology and performance of bulk heterojunction (BHJ) organic solar cells (OSCs), while volatile solid additives have been widely employed to control nanoscale phase separation during film formation. Concerns regarding reproducibility, residual solvent effects, and long-term stability have stimulated increasing interest in non-volatile solid additives. In recent years, solid additive engineering has emerged as a promising approach for modulating molecular packing, regulating phase separation, enhancing charge transport, and improving device stability. However, a systematic analysis of its material design principles and performance impact remains limited. This review summarizes recent progress in solid additive engineering for OSCs, categorizing reported additives into non-volatile, volatile and nanomaterials. The effects of these additives on key photovoltaic parameters, including open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and power conversion efficiency (PCE), are comparatively analyzed based on the reported data. Particular emphasis is placed on morphology and structural performance relationships and stability enhancement mechanisms. Finally, current challenges, including the lack of universal molecular design rules and limited mechanistic understanding of additive host interactions, are discussed, and future research directions are proposed. This review aims to provide a comprehensive perspective on the material-level role of solid additives and to guide the rational design of next-generation high-performance and stable organic solar cells. Full article

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this paper proposes a physics-constrained hybrid prediction model that integrates a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture with wide residual connections (WRC) and a physics-constrained loss (PCL). The proposed modeling approach combines real operational measurement data with high-resolution simulation data to enhance data diversity and improve generalization capability. The CNN–BiLSTM structure captures nonlinear temporal dependencies, while the WRC preserves critical low-level transient electrical features during deep temporal modeling. In addition, multiple physical constraints, including power balance, voltage conversion relationships, and battery state-of-charge (SOC) dynamics, are incorporated into the training process to enforce physically consistent predictions. The model is validated using charging and discharging experiments on a laboratory-scale SPS under both steady-state and transient conditions. Comparative results demonstrate that the proposed approach achieves higher prediction accuracy, improved dynamic stability, and faster recovery following disturbances compared with conventional data-driven models. These results indicate that physics-constrained deep learning provides an effective and interpretable modeling framework for SPS state prediction, supporting digital twin-oriented monitoring and real-time prediction applications. Full article