Search Results (165)

To address the issues of significant dc bus voltage and load fluctuations, as well as unstable power transmission in dual active bridge (DAB) converters within dc microgrid systems, this article proposes a segmented gain adjustment method based on multiplicative feedforward control (MFC-SGA). First, considering both steady-state and dynamic performance of DAB converters, two hybrid optimization control methods are proposed, and their advantages and disadvantages in terms of circuit parameter sensitivity and controller gain are analyzed. Second, to overcome the limitation of multiplicative feedforward control in light-load conditions due to restricted controller gain, the MFC-SGA method is introduced to enable adaptive parameter adjustment. Finally, an experimental prototype is built. Experimental results show that the MFC-SGA method is independent of inductance accuracy. When the operating condition changes, compared with the traditional method, the settling time is shortened by 60–83% and the overshoot is reduced by 37.5–62.5%; especially in light-load mode (10% of rated current), the dynamic response speed is improved by 68.75% compared with the MFC method, and the settling time is reduced from 32 ms to 10 ms. The experimental results verify the feasibility and effectiveness of the proposed method. Full article

The rapid proliferation of small satellite missions demands flight software that combines reliability, reusability, and rapid development cycles. NASA’s Core Flight System (cFS), with its layered architecture and component-based design, offers a promising solution. However, its resource-intensive design poses significant challenges for deployment on microcontroller (MCU) platforms commonly used in nanosatellites. This paper presents a comprehensive approach to porting cFS to the TMS570 safety microcontroller running FreeRTOS. We address critical challenges including Operating System Abstraction Layer (OSAL) adaptation for lightweight real-time operating systems and file system virtualization using RAM disk. As a core architectural contribution, we propose a hierarchical memory architecture that partitions high-speed internal RAM from external SDRAM, enabling all five cFE core services to operate within 256 KB on-chip RAM by offloading latency-tolerant data structures to SDRAM and releasing 37.5% of internal memory for mission applications. Performance evaluation yields two key quantitative findings: (1) Software Bus latency on SDRAM scales non-linearly from 1.85× to 7.67× relative to internal RAM as message size increases from 64 B to 4 KB, revealing that memory bandwidth—not fixed routing overhead—dominates large-transfer cost; (2) the cFS framework introduces a constant additive overhead of approximately 82.5 $\mathsf{\mu }$s per task cycle, independent of computational load, remaining below 0.1% of the execution budget at typical 1–10 Hz control rates. System stability is validated through 72 h continuous operation encompassing over 2.5 million task cycles with zero unplanned resets. This work establishes quantitative design guidelines—including memory placement criteria and task granularity thresholds—that provide a reusable technical pathway for deploying reliable, extensible flight software on resource-constrained embedded platforms. Full article

With the expansion of urban rail transit, increased train operation density, and the large-scale grid integration of renewable energy such as offshore photovoltaic power, traction power supply systems face stricter requirements for operational safety, power supply reliability and energy utilization efficiency. Offshore photovoltaic power, integrated into the traction power supply network via flexible DC transmission technology, promotes renewable energy consumption, but its random and volatile output overlaps with time-varying traction loads, increasing the complexity of DC-side fault characteristics and protection control. Flexible DC technology is a core direction for next-generation traction substations, and three-level converters (key energy conversion units) have advantages over traditional two-level topologies. However, their P-O-N three-terminal DC-side topology introduces new faults (e.g., PO/ON bipolar short circuits, O-point-to-ground faults), making traditional protection strategies ineffective. In addition, wide system current fluctuation (0.5–3 kA) and offshore photovoltaic power fluctuation easily cause fixed-threshold protection maloperation, and the coupling mechanism among modulation strategies, DC bus capacitor voltage dynamics and fault current paths is unclear. To solve these bottlenecks, this paper establishes a simulation model of the system based on the PSCAD/EMTDC(A professional simulation software for electromagnetic transient analysis in power systems V4.5.3) platform, analyzes the transient electrical characteristics of three-level converters under traction and braking conditions for typical faults, clarifies the coupling mechanism, proposes a condition-adaptive fault identification strategy, and designs a reconfigurable fault energy handling system with bypass thyristors and adaptive crowbar circuits. Simulation and hardware-in-the-loop (HIL) experiments show that the proposed scheme completes fault identification and protection within 2–3 ms, suppresses fault peak current by more than 70%, limits DC bus overvoltage within ±10% of the rated voltage, and has good post-fault recovery performance. It provides a reliable and engineering-feasible protection solution for related systems and technical references for similar flexible DC system protection design. Full article

With the aim of mitigating the impact of wind power integration and source-load-side uncertainties on an integrated energy system, we initially employed the Monte Carlo simulation in this study to randomly generate multiple wind power output/load scenarios in accordance with probability distribution functions. Additionally, we proposed a two-stage optimization method. In the first stage of our study, an enhanced African vulture optimization algorithm was applied to perform multi-objective optimization targeting fuel cost and carbon emissions across various scenarios, thereby solving the Pareto frontier to obtain multiple candidate solutions. In the study’s second stage, comprehensively considering fuel cost, carbon emission, and wind power penetration rate, evidential reasoning was utilized to determine the optimal operation strategy among the candidates. Finally, a combined heat and power system composed of the IEEE 30-bus system and a 32-node heating network was simulated. The results demonstrate that this decision-making approach can effectively reflect the merits of candidate solutions, thus validating the feasibility of the designed research methodology. Full article

To address the power and energy balancing challenges faced by high-penetration renewable energy systems under long-term intermittent output conditions, this study proposes a multi-source, multi-timescale collaborative dispatch strategy (2MT-S) integrating wind, solar, hydro, thermal, and hydrogen energy resources. First, a long-term-to-day-ahead coupled scheduling framework is established based on intermittent output duration forecasts (3-day/10-day). By integrating seasonal hydrogen storage and pumped-storage hydroelectric plants, this framework achieves comprehensive coordination among electrochemical storage, thermal power, and other flexible resources. Second, a multi-time-horizon optimization model is developed to simultaneously minimize system operating costs and load curtailment costs. This model dynamically adjusts day-ahead scheduling boundary conditions based on long-term and short-term scheduling results, enabling cross-period resource complementarity during wind and photovoltaic generation troughs. Finally, comparative analysis on an enhanced IEEE 30-bus system demonstrates that compared to traditional day-ahead scheduling, this strategy significantly reduces renewable energy curtailment rates and load curtailment volumes during sustained low-generation periods, fully validating its significant advantages in enhancing power supply reliability and economic benefits. Full article

To enhance the power supply resilience of distribution networks with high-penetration distributed photovoltaic (PV) integration during extreme disasters, deploying Mobile Energy Storage Systems (MESSs) proves to be an effective countermeasure. This paper proposes an optimized operational strategy for distribution networks, integrating coordinated clustering of distributed PV systems and MESS operation to ensure power supply during both pre-disaster prevention and post-disaster restoration phases. In the pre-disaster prevention phase, an improved Louvain algorithm is first applied for PV clustering to improve source-load matching efficiency within each cluster, thereby enhancing intra-cluster power supply security. Subsequently, under the worst-case scenarios of PV output fluctuations, a robust optimization algorithm is utilized to optimize the pre-deployment scheme of MESS. In the post-disaster restoration phase, cluster re-partitioning is performed with the goal of minimizing load shedding to ensure power supply, followed by reoptimizing the scheduling of MESS deployment and its charging/discharging power to maximize the improvement of load power supply security. Simulations on a modified IEEE 123-bus distribution network, which includes two MESS units and twenty-four PV systems, demonstrate that the proposed strategy improved the overall restoration rate from 68.98% to 86.89% and increased the PV utilization rate from 47.05% to 86.25% over the baseline case, confirming its significant effectiveness. Full article

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an efficient analytical framework that incorporates the actions and capacity constraints of regulation units. Firstly, a dual dynamic piecewise linear power injection model is established based on “frequency deviation interval stratification and unit limit-reaching sequence ordering,” clarifying the hierarchical activation sequence of “loads first, followed by conventional units, and finally automatic generation control (AGC) units” along with the coupled adjustment logic upon reaching limits, thereby accurately reflecting the actual frequency regulation process. Subsequently, this model is integrated with the State-Independent Linearized Power Flow (DLPF) model to develop a segmented stochastic power flow framework. For the first time, a deep integration of unscented transformation (UT) and regulation-aware power allocation is achieved, coupled with the Nataf transformation to handle correlations among random variables, forming an analytical framework that balances accuracy and computational efficiency. Case studies on the New England 39-bus system demonstrate that the proposed method yields results highly consistent with those of Monte Carlo simulations while significantly enhancing computational efficiency. The DLPF model is validated to be applicable under scenarios where voltage remains within 0.95–1.05 p.u., and line transmission power does not exceed 85% of rated capacity, exhibiting strong robustness against parameter fluctuations and capacity variations. Furthermore, the method reveals voltage distribution patterns in wind-integrated power systems, providing reliable support for operational risk assessment in grids with high shares of renewable energy. Full article

Accurate multi-node energy market forecasting is critical for secure and economic grid operation under increasing penetration of renewable energy and electric vehicles. This paper proposes a physics-aware spatiotemporal forecasting framework that integrates Graph Convolutional Networks (GCNs) for modeling network-level spatial dependencies with a self-attention mechanism for capturing long-range temporal correlations. Unlike existing GCN + RNN or attention-based forecasting approaches, physical feasibility is enforced during learning through structured penalty terms reflecting power balance, generation limits, EV state-of-charge dynamics, and AC load flow constraints, rather than via post-processing optimization. The model is evaluated on a synthetic IEEE 24-bus benchmark with realistic load scaling, renewable variability, and EV charging profiles. Results show a mean squared error of 1.84 MW² and a 7–10% reduction in forecasting error relative to baseline ARIMA and LSTM models, while maintaining constraint violation rates below 5%. Multi-step forecasting experiments demonstrate stable error growth under high volatility conditions. The proposed framework establishes a bridge between purely data-driven forecasting and physically consistent grid-aware prediction, offering a scalable foundation for operationally feasible energy market forecasting. Full article

Rapid and accurate identification of important lines in power grids is crucial for enhancing grid reliability and preventing large-scale blackouts. This paper proposes a method for identifying important lines in power systems using an improved ProfitLeader (IPL) algorithm. First, a correlation network integrating power flow dynamics and topological structure is constructed. Then, by incorporating line weights and directionality, the method overcomes the limitation of traditional ProfitLeader algorithms that only consider node out-degree. Finally, the constructed correlation network and improved algorithm are applied to identify important lines. Comparative studies with other common identification methods on the IEEE 39-bus system show that after attacking the top seven important lines identified by the proposed algorithm, the number of electrical islands in the system increases significantly, and the remaining load rate drops to 43.7%. These results verify the accuracy and effectiveness of the proposed method. Full article

To prevent swine fever transmission, swine farms in China adopt enclosed management, making strict farm personnel biosecurity essential for minimizing the risk of pathogen introduction. However, current shower-in procedures and personnel movement records on many farms still rely on manual logging, which is prone to omissions and cannot support enterprise-level supervision. To address these limitations, this study develops a digital personnel management system designed specifically for the changing-room environment that forms the core biosecurity barrier. The proposed three-tier architecture integrates distributed identification terminals, local central controllers, and a cloud-based data platform. The system ensures reliable identity verification, synchronizes templates across terminals, and maintains continuous data availability, even in unstable network conditions. Fingerprint-based identity validation and a lightweight CAN-based communication mechanism were implemented to ensure robust operation in electrically noisy livestock facilities. System performance was evaluated through recognition tests, multi-frame template transmission experiments, and high-load CAN/MQTT communication tests. The system achieved a 91.4% overall verification success rate, lossless transmission of multi-frame fingerprint templates, and stable end-to-end communication, with mean CAN-bus processing delays of 99.96 ms and cloud-processing delays below 70.7 ms. These results demonstrate that the proposed system provides a reliable digital alternative to manual personnel movement records and shower duration, offering a scalable foundation for biosecurity supervision. While the present implementation focuses on identity verification, data synchronization, and calculating shower duration based on the interval between check-ins, the system architecture can be extended to support movement path enforcement and integration with wider biosecurity infrastructures. Full article

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience. Full article

Power semiconductor devices are fundamental components in modern electronic power conversion. In applications demanding high power density and efficiency, the choice between silicon (Si) IGBTs and Silicon Carbide (SiC) MOSFETs is critical. SiC MOSFETs, owing to their high critical electric field, superior thermal conductivity, wide band gap, and low power loss, realize significant performance improvements and compact design. This work presents a comprehensive, simulation-driven comparative investigation under identical setups, evaluating both technologies across various parameters. The effects of temperature variations on gate-source threshold voltage drift, current slew rate, device stress, and energy dissipation during switching transitions are evaluated. Furthermore, the characteristic switching behavior when the DC-bus voltage, gate resistance, and load current are varied is investigated. This study addresses a current scarcity of systematic investigation by presenting a comprehensive comparative evaluation of switching losses and efficiency across varied operating conditions, providing validated conclusions for the design of advanced WBG converters. The results demonstrate that SiC exhibits lower losses and faster switching speeds than Si IGBTs, with minimal temperature-dependent loss variations, unlike Si devices, whose losses rise significantly with temperature. Si shows distinct tail currents during turn-off, absent in SiC devices. A conclusive comparative evaluation of switching energy losses under varied operating conditions demonstrates that SiC devices can effectively retrofit Si counterparts for fast, low-loss, high-efficiency applications. Full article

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over $(r_{\mathrm{PV}},P_{\mathrm{ext}})$ exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments. Full article

With the rapid increase in the renewable energy penetration rate in distribution networks, the volatility and uncertainty on the power supply side have become prominent; thus, it is urgent to fully utilize the regulation potential of the flexible load on the user side to maintain the dynamic balance of power. A multi-level evaluation method for flexible load regulation potential based on ensemble clustering is proposed in the paper. First, a data-driven approach based on ensemble clustering is adopted to quantify the user-level regulation potential of flexible load. Second, the bus-level regulation potential of the flexible load is obtained by aggregation calculation. Finally, a quantitative evaluation of the system-level regulation potential of flexible load in the distribution network is realized by constructing three optimization models with different objectives. Case studies show that the proposed method can effectively evaluate the regulation potential of flexible load in the distribution network from multiple levels, i.e., user level, bus level, and system level. Full article

The regulation of voltage in transmission networks is becoming increasingly complex due to the dynamic behavior of modern power systems and the growing penetration of renewable generation. This study presents a comparative analysis of three artificial intelligence approaches—Deep Q-Learning (DQL), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—for training agents capable of performing autonomous voltage control. A unified neural architecture was implemented and tested on the IEEE 30-bus system, where the agent was tasked with adjusting reactive power set points and transformer tap positions to maintain voltages within secure operating limits under a range of load conditions and contingencies. The experiments were carried out using the GridCal simulation environment, and performance was assessed through multiple indicators, including convergence rate, action efficiency, and cumulative reward. Quantitative results demonstrate that PSO achieved 3% higher cumulative rewards compared to GA and 5% higher than DQL, while requiring 8% fewer actions to stabilize the system. GA showed intermediate performance with 6% faster initial convergence than DQL but 4% more variable results than PSO. DQL demonstrated consistent learning progression throughout training, though it required approximately 12% more episodes to achieve similar performance levels. The quasi-dynamic validation confirmed PSO’s advantages over conventional AVR-based strategies, achieving voltage stabilization approximately 15% faster. These findings underscore the potential of neuroevolutionary algorithms as competitive alternatives for advanced voltage regulation in smart grids and point to promising research avenues such as topology optimization, hybrid metaheuristics, and federated learning for scalable deployment in distributed power systems. Full article