Search Results (1,230)

This paper presents the design and implementation of a control algorithm for power converters in a microgrid, with the main objective of providing the flexibility to adjust the system inertia. The increasing integration of renewable energy sources in microgrids has driven the development of advanced control techniques to ensure stability and power quality. The proposed algorithm combines droop control, synchronverter dynamics, and virtual impedance to achieve a robust and efficient control strategy. Simulations were conducted to validate the algorithm’s performance, demonstrating its capability to maintain voltage within acceptable limits and improve the inertial response of the microgrid. The results contribute to the advancement of intelligent and resilient microgrid development, which is essential for the transition towards a more sustainable energy system. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

This paper proposes a nonlinear control strategy for a microgrid, comprising a PV generator, wind turbine, battery, solid oxide fuel cell (SOFC), electrolyzer, and a three-phase four-leg voltage source inverter (VSI) with an LC filter. The microgrid is designed to supply unbalanced AC loads while maintaining high power quality. To address chattering and enhance control precision, a super-twisting algorithm (STA) is integrated, outperforming traditional PI, IP, and classical SMC methods. The four-leg VSI enables independent control of each phase using a dual-loop strategy (inner voltage, outer current loop). Stability is ensured through Lyapunov-based analysis. Scalar PWM is used for inverter switching. The battery, SOFC, and electrolyzer are controlled using integral backstepping, while the SOFC and electrolyzer also use Lyapunov-based voltage control. A hybrid integral backstepping–STA strategy enhances PV performance; the wind turbine is managed via integral backstepping for power tracking. The system achieves voltage and current THD below 0.40%. An energy management algorithm maintains power balance under variable generation and load conditions. Simulation results confirm the control scheme’s robustness, stability, and dynamic performance. Full article

A multidisciplinary investigation was conducted in southwestern Sicily, near the seismically active Belice Valley, based on the analysis of morphostructural features. These were observed as open fractures between 2014 and 2017; they were subsequently filled anthropogenically and then reactivated during a seismic swarm in 2019. We generated a seismic event distribution map to analyze the location, magnitude, and depth of earthquakes. This analysis, combined with multitemporal satellite imagery, allowed us to investigate the spatial and temporal relationship between seismic activity and fracture evolution. To investigate the spatial variation in thickness of the superficial cover and to assess the depth to the underlying bedrock or stiffer substratum, 45 Horizontal-to-Vertical Spectral Ratio (HVSR) ambient noise measurements were conducted. This method, which analyzes the resonance frequency of the ground, produced maps of the amplitude, frequency, and vulnerability index of the ground (K_g). By inverting the HVSR curves, constrained by Multichannel Analysis of Surface Waves (MASW) results, a subsurface model was created aimed at supporting the structural interpretation by highlighting variations in sediment thickness potentially associated with fault-controlled subsidence or deformation zones. The surface investigation revealed depressed elliptical deformation zones, where mainly sands outcrop. Grain-size and morphoscopic analyses of sediment samples helped understand the processes generating these shapes and predict future surface deformation. These elliptical shapes recall the liquefaction process. To investigate the potential presence of subsurface fluids that could have contributed to this process, Electrical Resistivity Tomography (ERT) was performed. The combination of the maps revealed a correlation between seismic activity and surface deformation, and the fractures observed were interpreted as inherited tectonic and/or geomorphological structures. Full article

Empirical research on the impact of the digital economy on sustainable development is hampered by severe methodological challenges. Discrepancies in the theoretical foundations and construction logic of measurement frameworks have led to diverse and often conflicting conclusions, hindering the systematic accumulation of knowledge. This study aims to address this critical gap by proposing a new, logically consistent measurement framework. To overcome the existing limitations, we construct a functional deconstruction framework grounded in General-Purpose Technology (GPT) theory and a “stock–flow” perspective. This framework deconstructs the digital economy into a neutral “digital infrastructure” (stock platform) and two forces reflecting its inherent duality: a “consumption force” (digital industrialization) and an “empowerment force” (industrial digitalization). Based on this, we develop a measurement system adhering to the principle of “logical purity” and apply a “two-step entropy weighting method with annual standardization” to assess 30 provinces in China from 2012 to 2023. Our analysis reveals a multi-scalar evolution. At the micro level, we identified four distinct provincial development models and three evolutionary paths. At the macro level, we found that the overall inter-provincial disparity followed an inverted U-shaped trajectory, with the core contradiction shifting from an “access gap” to a more profound “application gap.” Furthermore, the primary driver of this disparity has transitioned from being “empowerment-led” to a new phase of a “dual-force rebalancing.” The main contribution of this study is the provision of a new analytical tool that enables a paradigm shift from “aggregate assessment” to “structural diagnosis.” By deconstructing the digital economy, our framework allows for the identification of internal structural imbalances and provides a more robust and nuanced foundation for future causal inference studies and evidence-based policymaking in the field of digital sustainability Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

To address the issues of traditional linear inversion methods, such as their dependence on initial models and the high computational cost of Jacobian matrix calculations, this study conducts inversion research on vertical electrical sounding data based on the backpropagation (BP) neural network combined with the Particle Swarm Optimization (PSO) algorithm. First, two-layer and three-layer horizontally layered geoelectric models were constructed to generate the sample data required for neural network training. Secondly, the PSO-BP neural network model was employed to perform test inversions. The inversion results demonstrate that both neural network methods can successfully invert apparent resistivity data into corresponding geoelectric model parameters, thereby validating the correctness of the PSO-BP neural network inversion approach. Finally, the PSO-BP neural network method was applied to training and inversion of field-measured apparent resistivity data. A comparison between the inversion results of the PSO-BP neural network and those of the conventional BP neural network revealed that the PSO-BP neural network yields superior inversion results. This further confirms the reliability, effectiveness, and practical applicability of the proposed inversion method. The work presented in this study provides a novel approach and perspective for the inversion of vertical electrical sounding data. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

The integration of Internet of Things (IoT) technologies into islanded microgrids has increased their vulnerability to cyberattacks, particularly those targeting critical components such as power converters within an islanded AC microgrid. This study investigates the impact of False Data Injection (FDI) and Denial of Service (DoS) attacks on various power converters, including DC–DC boost converters, DC–AC converters, battery inverters, and DC–DC buck–boost converters, modeled in MATLAB/Simulink. A dataset of healthy and compromised operational parameters, including voltage and current, was generated under simulated attack conditions. To enhance system resilience, a deep learning-based detection and classification framework was proposed. After evaluating various deep learning models, including Deep Neural Networks (DNNs), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Long Short-Term Memory (LSTM), and Feedforward Neural Networks (FNNs), the final system integrates an FNN for rapid attack detection and an LSTM model for accurate classification. Real-time simulation validation demonstrated a detection accuracy of 95% and a classification accuracy of 92%, with minimal computational overhead and fast response times. These findings emphasize the importance of implementing intelligent and efficient cybersecurity measures to ensure the secure and reliable operation of islanded microgrids against evolving cyberattacks. Full article

The Dynamic Voltage Restorer (DVR), which is connected in series between the power grid and the load, can rapidly compensate for voltage disturbances to maintain stable voltage at the load end. To enhance the energy supply capacity of the DVR and utilize its shared circuit topology with photovoltaic (PV) inverters—which enables the dual functions of voltage compensation and PV-storage power generation—this study integrates PV and energy storage as a coordinated energy unit into the DVR, forming a PV-storage-integrated DVR system. The core innovation of this system lies in extending the voltage disturbance detection capability of the DVR to include harmonics. By incorporating a Butterworth filtering module and voltage fluctuation tracking technology, high-precision disturbance identification is achieved, thereby supporting power balance control and functional coordination. Furthermore, a multi-mode-power coordinated regulation method is proposed, enabling dynamic switching between operating modes based on PV output. Simulation and experimental results demonstrate that the proposed system and strategy enable smooth mode transitions. This approach not only ensures reliable voltage compensation for sensitive loads but also enhances the grid-support capability of PV systems, offering an innovative technical solution for the integration of renewable energy and power quality management. Full article

This paper presents a novel approach to address the challenges of complex terrain, dynamic wind fields, and multi-objective constraints in multi-UAV collaborative path planning for forest firefighting missions. An extensible algorithm, termed Parallel Vectorized Differential Evolution-based Multi-Objective Jellyfish Search (PVDE-MOJS), is proposed to enhance path planning performance. A comprehensive multi-objective cost function is formulated, incorporating path length, threat avoidance, altitude constraints, path smoothness, and wind effects. Forest-specific constraints are modeled using cylindrical threat zones and segmented wind fields. The conventional jellyfish search algorithm is then enhanced through multi-core parallel fitness evaluation, vectorized non-dominated sorting, and differential evolution-based mutation. These improvements substantially boost convergence efficiency and solution quality in high-dimensional optimization scenarios. Simulation results on the Phillip Archipelago Forest Farm digital elevation model (DEM) in Australia demonstrate that PVDE-MOJS outperforms the original MOJS algorithm in terms of inverted generational distance (IGD) across benchmark functions UF1–UF10. The proposed method achieves effective obstacle avoidance, altitude optimization, and wind adaptation, producing uniformly distributed Pareto fronts. This work offers a viable solution for emergency UAV path planning in forest fire rescue scenarios, with future extensions aimed at dynamic environments and large-scale UAV swarms. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

Ultrafast lasers operating at 740 nm and 820 nm have attracted widespread attention as two-photon light sources for the detection of biological metabolism. Here, we report on a solid-like quartz-encapsulated femtosecond laser with a repetition rate of 80 MHz, delivering 740 nm and 820 nm femtosecond laser pulses. This home-built laser system was realized by employing an erbium-doped 1560 nm fiber laser as the fundamental laser source. A quartz-encapsulated nonlinear frequency conversion stage, consisting of a second-harmonic generation (SHG) stage and self-phase modulation (SPM)-based nonlinear spectral broadening stage, was utilized to deliver 30 mW, 53.7 fs, 740 nm laser pulses and the 15 mW, 60.8 fs, 820 nm laser pulses. Further imaging capabilities of both wavelengths were validated using a custom-built inverted two-photon microscope. Clear imaging results were obtained from mouse kidney sections and pollen samples by collecting the corresponding fluorescence signals. The achieved results demonstrate the great potential of this laser source for advanced two-photon microscopy in metabolic detection. Full article

Land use efficiency (LUE) serves as a crucial nexus between economic development and sustainable resource management, directly influencing urban production–consumption systems. While economic development stages (EDSs) reflect a region’s environmental carrying capacity and profoundly affect LUE, the specific mechanisms governing this relationship remain unclear. In this study, we combined multi-source data to portray the spatiotemporal patterns of EDSs and LUE in 276 Chinese cities from 1995 to 2020, and we identified the nonlinear effects of EDSs on LUE. Based on the fine-scale LUE, it is confirmed that the older the age of urban land generation, the higher the LUE, laying a theoretical foundation for subsequent research. Simultaneously, the EDS continues to be upgraded, with approximately 70% of cities reaching the post-industrialization stage or higher by 2020. The results of partial dependency plots (PDPs) revealed that the EDS has a positive impact on LUE. From the perspective of different urban scales, the higher the EDSs of supercities, type I large cities, type II large cities, and type II small cities, the greater the positive impact on LUE, whereas the impact patterns at other urban scales follow an inverted U-shape. These findings carry important implications for sustainable spatial development, particularly in optimizing land resource allocation to assist the shift to more efficient production systems and responsible consumption patterns. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article