Search Results (1,514)

Commutation failures (CFs), which occur when current transfer between valves in line-commutated converter high-voltage direct current (LCC-HVDC) systems is disrupted, pose a challenge in weak alternating current (AC) networks. This paper introduces a coordinated control strategy that combines a fuzzy self-tuning proportional-integral (PI) controller (FSTPIC) and a static synchronous compensator (STATCOM) device to mitigate CFs and enhance system stability. The approach applies the FSTPIC to both converters of the HVDC link, while the STATCOM at the inverter side delivers dynamic reactive power and voltage support during AC faults. We test this strategy on the CIGRE HVDC benchmark system using MATLAB/SIMULINK simulations. The results demonstrate that the proposed method significantly reduces CFs, mitigates transient oscillations, and shortens recovery time compared to conventional control techniques. This coordinated control boosts voltage stability and the system’s ability to ride through faults, confirming its superiority under various fault scenarios in weak-grid conditions. Full article

A strategy for controlling DC boost converters under disturbances is proposed through a disturbance-resistant reinforcement learning method. An iterative training process is designed using model-free deep reinforcement learning. A nonlinear disturbance observer is integrated to enhance the controller’s adaptability to disturbances. The proposed neural network controller achieves a maximum voltage deviation of 4.2% of the reference value, and a settling time of 6.5 ms in simulation studies. During experimental validation, the voltage deviation is limited to 6.0% with a settling time of 8 ms under the same operating condition. Simulation and experimental results demonstrate that the proposed control strategy is able to stabilize the output voltage under various disturbances, offering better performance under large-signal fluctuation. This work extends the practical deployment of reinforcement learning in the field of nonlinear control. Full article

Short-horizon equity forecasting remains challenging because daily prices are noisy, heavy-tailed, and subject to structural breaks and regime shifts. We develop a fully automated, reproducible, and leakage-controlled multi-model pipeline for daily forecasting with horizon-specific configuration selection. The task is formulated as predicting cumulative H-day log-returns from OHLCV-derived information and converting them to implied price forecasts. All model families share a homologated design: causal feature construction, a strictly chronological split with an explicit purging rule to prevent label-window overlap for multi-day targets, training-only robustification (winsorization and adaptive clipping), and a unified metric suite computed consistently in return and price spaces. The framework benchmarks transparent baselines (zero- and mean-return), gradient-boosted trees (XGBoost), and deep temporal models (LSTM and CNN/TCN). Lookback length $L\in \{60,180,500\}$ is selected via an internal walk-forward procedure on the pre-evaluation block, and final performance is reported on an external hold-out segment (last 15% of instances). Experiments on daily data for MT, DELL, and the S&P 500 index (through 3 February 2026) show that all families achieve similarly strong price-level fit at $H=1$, largely driven by persistence in the price process, while separation across families becomes more visible at $H=5$. However, predictive performance in return space remains weak, with $R^2$ close to zero or negative, and Diebold–Mariano tests do not provide consistent evidence of statistical superiority over naive benchmarks. Under an operational rule that minimizes hold-out RMSE on the price scale, selected models are asset- and horizon-dependent, supporting horizon-wise selection rather than a single global architecture. Overall, the primary contribution lies in the proposed leakage-controlled evaluation and benchmarking framework rather than in demonstrating consistent predictive gains in financial time series forecasting. Full article

During long-term operation, power electronic converters are jointly affected by component degradation and operational disturbances, leading to pronounced nonstationary and multi-scale characteristics in output-voltage signals, which pose challenges for fault prediction. To address the degradation forecasting problem of Boost converter output voltage, this paper proposes a multi-scale temporal modeling method that integrates multivariate variational mode decomposition, distribution entropy-based complexity features, and a temporal convolutional network. Multivariate variational mode decomposition is employed to achieve frequency-aligned decomposition of the voltage signal, enabling effective separation of dynamic components at different scales. Distribution entropy is then introduced to characterize the evolution of local structural complexity in each mode, and multi-channel complexity feature sequences are constructed accordingly. Based on these features, a temporal convolutional network is used to perform unified modeling of short-term fluctuations and long-term degradation trends. Experimental results demonstrate that the proposed approach achieves consistently high accuracy across multiple independent runs, with average RMSE ranging from 0.0111 to 0.0179 and average MAPE from 1.15% to 1.84%. The low standard deviations further confirm its robustness for degradation trend prediction under varying operating conditions. Full article

This research experiment aimed to transform multicomponent Ni-based superalloys produced with e-waste additives into corrosion-resistant materials via heat treatment. The experiment involved a two-hour heat treatment of as-cast samples at 1000 °C in an argon atmosphere, followed by quenching in water and characterization by scanning electron microscopy coupled to energy-dispersive spectroscopy (SEM-EDS). Thereafter, the corrosion characteristics of the heat-treated and non-heat-treated samples were studied in 0.5 M sulfuric acid using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP). Results showed that the FCC gamma solid-solution matrix in the microstructure was homogenized by heat treatment. A continuous grain boundary M₂₃C₆ and interdendritic M₆C were redistributed into discrete particles after the heat treatment, which facilitated the reduction in galvanic pathways and boosted corrosion resistance. The heat-treated samples exhibited nobler OCP, increased low-frequency impedance, reduced corrosion current density, a broader passive range, and increased breakdown potential. These findings have proved that it is feasible to convert scrap to service affordably. Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

This paper presents an adaptive talkative power (TP) framework that enables simultaneous high-efficiency power transfer and reliable data communication under time-varying load conditions. A high-frequency TP-based bidirectional boost converter employing a SiC-based zero voltage switching–quasi square wave (ZVS-QSW) topology is proposed, incorporating closed-loop online efficiency optimization. Data transmission is realized through adaptive switching-frequency modulation at the transmitter, allowing information encoding while preserving optimal power transfer efficiency. To support reliable data detection under unknown and non-constant load conditions, an adaptive receiver architecture is developed that extracts information from output voltage ripple variations induced by frequency modulation. Owing to the nonlinear and complex nature of the ripple characteristics, a supervised machine-learning-based classification approach is employed for data detection, eliminating the need for prior knowledge of converter parameters and overcoming the limitations of conventional maximum-likelihood detection methods. The proposed system is validated through real-time simulations using a dSPACE MicroLabBox system in conjunction with MATLAB/Simulink R2025b. Simulation results demonstrate power transfer efficiencies approaching 98% while enabling reliable and efficient data transmission across a wide range of operating conditions, including varying conversion ratios and dynamic load variations, thereby confirming the effectiveness and robustness of the proposed TP-based power and data transmission scheme. Full article

A triple phase shift (TPS) modulation strategy is proposed for a three-port active bridge (TAB) converter in shipboard zonal DC systems. Unlike traditional multi-port converters, the TAB realizes voltage conversion and bidirectional power conversion under TPS modulation. It exhibits superior performance in reducing control complexity, enhancing fault-tolerant capability, and extending the zero-voltage switching (ZVS) region under normal and fault operation modes. To further enhance its conversion efficiency, a deep reinforcement learning optimization approach based on the deep deterministic policy gradient (DDPG) algorithm is introduced to adaptively optimize TPS control parameters and minimize the overall power losses of the converter. To verify the proposed TPS modulation and DDPG-based optimization strategy for the TAB converter topology, a corresponding hardware prototype is built and experimentally tested under different operating conditions. Experimental results demonstrate that the TAB architecture with DDPG optimization effectively reduces current stress and power loss, boosting the converter’s maximum efficiency to 96.9% under normal mode and a 3% efficiency gain after fault isolation. Full article

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

This research evaluated the efficacy of using two types of biochar (non-modified and acidified) from date palm residues (fronds, leaves, pits) as soil amendments for enhancing soil fertility and maize growth. These biochars were produced through slow pyrolysis under oxygen-limited conditions at 500 °C. Our innovative approach was to minimize gas emissions by converting smoke into liquid fertilizer (LS), which was expected to improve seed germination and early plant growth stages. To assess this aim, a completely randomized experiment was conducted under lab conditions, in which 10 maize seeds were placed on double filter papers in Petri dishes and then exposed to seven concentrations of LS (0.0, 0.01, 0.10, 1.0, 10 and 100%, using distilled water for dilution v/v). The LS contains nutrients and bioactive compounds that may enhance seed germination and early plant growth at low concentrations, whereas higher concentrations may cause phytotoxic effects. Results showed that liquefied smoke at 0.1% increased the absolute percentage of maize germination from 75% (control) to 100% and achieved the highest root length of 9.80 cm. Acidified biochars at 5% reduced soil pH from 8.87 to 8.12 and enhanced potassium availability to 87.93 mg kg⁻¹. Conversely, the non-modified biochars contributed to further increases in soil organic matter (up to 1.02%), nitrogen, and phosphorus. In addition, the application of acidified leaf biochar (5%) enhanced maize shoot growth by 133%, chlorophyll content by 39%, and potassium uptake by 110%. This research establishes a scalable approach for converting agricultural waste into climate-resilient resources, effectively addressing soil degradation in arid environments, boosting crop resilience, and furthering the objectives of a circular bioeconomy. Full article

This study investigated the feasibility of a wearable wind energy-harvesting system integrated into a motorcycle helmet that converts riding-induced airflow into storable electrical energy. A compact horizontal-axis turbine-based system was designed and optimized through staged experiments focusing on generator selection, housing geometry, rotor configuration, and circuit-connected performance. A medium-scale generator, diffuser-type housing (Hd), and eight-blade pinwheel rotor (Rb) were identified as the most suitable combination for helmet-scale integration. The final prototype incorporated two side-mounted turbine modules, a crown-mounted harvesting–boost circuit, and a detachable rechargeable battery pack within a full-face helmet platform. In a field-based riding experiment, the prototype produced mean outputs of 3.99 V, 39.51 mA, and 157.64 mW at 30 km/h; 4.43 V, 43.48 mA, and 192.61 mW at 40 km/h; and 5.45 V, 53.53 mA, and 291.73 mW at 50 km/h. A static wearability evaluation with six participants indicated no obvious discomfort under a quasi-riding posture. These findings support the practical feasibility of helmet-integrated wind energy harvesting as an auxiliary power source for low-power wearable electronics, while highlighting the need for future studies on aerodynamic validation, dynamic wearability, acoustic burden, and safety-oriented structural refinement. Full article

Rapid advancements in biotechnology have enabled biomanufacturing to emerge as a feasible approach for industrial chemical production. By harnessing synthetic biology and metabolic engineering, engineered microbial cell factories can convert renewable resources into valuable chemicals, providing a sustainable alternative to traditional chemical methods. This study focuses on the microbial production of retinal, an important retinoid used in pharmaceuticals, food, and cosmetics. The oleaginous yeast Rhodotorula toruloides NP11 was genetically modified to synthesize retinal by incorporating and optimizing three β-carotene 15,15′-dioxygenase genes from various sources. Several genetic modifications were made to enhance retinal yield, including the overexpression of isopentenyl-diphosphate isomerase (IDI1), geranylgeranyl diphosphate synthase (BTS1), phytoene synthase (CARRP), and phytoene dehydrogenase (CARB), which led to increased β-carotene levels and boosted retinal production. Furthermore, fermentation conditions such as temperature, antioxidants, and extractants were fine-tuned. The engineered strain Rt13 ultimately achieved a maximum retinal concentration of 20.38 mg/L through fed-batch fermentation. This study highlights the potential of R. toruloides as a cell factory for terpenoid biosynthesis, providing valuable insights for future metabolic engineering endeavors. Full article

This work introduces a systematic small-signal modeling framework for a family of non-isolated high step-up dc–dc converters based on the multistate switching cell (MSSC) operating in continuous conduction mode (CCM). By analyzing the current and voltage waveforms associated with the switching cell, an averaged circuit model based on the pulse width modulation (PWM) switch technique is derived. The proposed method relies only on basic circuit principles, avoiding complex matrix manipulations. To validate the theoretical assumptions, a non-isolated dc–dc boost converter with a high voltage gain is evaluated, and its response is compared with that of the derived model. Full article

This paper explores a new switching strategy for a recently proposed quadratic boost converter. The topology under study is a high-step-up DC–DC converter with a configuration that allows a portion of the processed energy to be used in what we call a non-series transfer. This characteristic reduces the amount of power processed redundantly. This converter, called a Quadratic Boost Converter based on Non-Series Energy Transfer (QBC-NSET), also has a non-pulsating input current, which is especially desirable for applications like photovoltaic and fuel-cell sources. This paper proposes a different switching strategy that reduces the output voltage ripple without increasing the switching frequency and without increasing the stored energy (inductance in inductors or capacitance in capacitors). The converter has two transistors, originally operated with synchronized signals; the proposed strategy provides independent switching signals with a phase shift between them. This enables the output capacitor to charge in a different switching state, producing a smaller voltage ripple while preserving the advantages of the topology originally presented. Steady-state analysis and voltage gain derivations confirm that the fundamental conversion characteristics remain unchanged. Experimental results obtained from a laboratory prototype validate the effectiveness of the proposed approach, demonstrating the reduction in the output voltage ripple. Full article

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46 μs settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article