Search Results (252)

This article introduces a new Multi-Source DC-DC converter-based smart energy management system on a common DC bus architecture, utilizing solar PV and wind sources for electric vehicle applications. The common DC bus enables coordinated power flow control among multiple sources while maintaining modularity and flexibility. To promote efficient battery charging and discharging, as well as enhanced protection from faults, an artificial neural network (ANN) approach has been incorporated. The main function of the ANN controller is to detect faults in the EV battery for timely intervention. Compared to existing topologies, its coordinated integration and control can operate effectively under dynamic conditions and improve stability. Additionally, the article presents the operating principle, modes of operation, design analysis, and control strategy. The simulation results of the proposed system are evaluated through MATLAB Simulink software 2024b. Furthermore, a 200 W laboratory prototype was developed to validate the system’s dynamic performance under various operating conditions. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

This study systematically analyzes the spatio-temporal evolution, trade-offs, synergies and driving mechanisms of five ecosystem services (ESs) in Fujian Province (carbon storage, CS; habitat quality, HQ; sediment delivery ratio, SDR; water yield, WY; food provision, FP) based on multi-source data from 2003, 2013 and 2023 by adopting the InVEST model, Spearman correlation analysis, geographically weighted regression (GWR), self-organizing maps (SOM) and geographic detectors. Results show that: (1) ESs present a spatial pattern of “high in northwest and low in southeast” in Fujian; CS, HQ and FP show an overall decline, while SDR and WY increase significantly. (2) ES trade-offs and synergies have obvious scale effects and spatial heterogeneity, with stronger relationship intensity at the county level than the grid level, and FP generally shows a trade-off relationship with other services. (3) Land use is the key driving factor for CS, FP and HQ; precipitation dominates the changes in WY and SDR; and dual-factor interactions generally enhance the explanatory power of ES changes. The findings enrich the theoretical system of multi-scale ES trade-off and synergy research under rapid urbanization and provide a scientific basis for sustainable territorial spatial planning and differentiated ecological governance in Fujian. Meanwhile, the research framework can serve as a reference for ES management in other coastal mountainous regions worldwide, contributing to the realization of regional sustainable development goals (SDGs). Full article

To address the limitations of existing wearable gait recognition, such as drift in static actions and difficulty in recognizing transition states, this paper proposed a gait recognition system based on the data fusion of MEMS Inertial Measurement Units (IMUs) and flexible plantar pressure sensors. A low-power wearable device comprising four inertial and two pressure sensing nodes was developed to achieve synchronized multi-source data collection. Regarding the algorithm, a sensor-characteristic-based two-stage hierarchical framework was constructed. The first stage utilized plantar pressure features to efficiently decouple static postures from dynamic gaits. The second stage employed a lightweight Support Vector Machine combined with a Finite State Machine for static and transitional actions, while an ensemble learning model based on Soft Voting was used for complex dynamic gaits. Experimental results under Leave-One-Out Cross-Validation demonstrate a comprehensive recognition accuracy of 96.17%, with 100% accuracy for standing and 97% for sit-to-stand transitions. These findings validate the significant advantages of the multi-modal fusion approach in enhancing the robustness and generalization capabilities of gait recognition. Full article

As small modular reactors (SMRs) evolve towards longer lifespans, autonomous operation, and high reliability, the accuracy and reliability of sensor data are crucial for ensuring the safe operation of nuclear power systems. To improve the accuracy of multi-source sensor fault detection and localization in small reactors, this paper proposes a multi-scale cross-correlation-based convolutional autoencoder (MSCC-CAE) framework. First, multiple sensor cross-correlation matrices are constructed across multiple time scales to explicitly characterize the dynamic coupling relationships between heterogeneous sensors. These multi-scale correlation features can effectively capture both short- and long-term dependencies among sensors. Then, a convolutional autoencoder is used to compress and reconstruct the correlation matrix, thereby learning low-dimensional discriminative representations for fault detection. To enhance the stability and generalization of the proposed framework, a multi-strategy improved quantum particle swarm optimization (MSQPSO) algorithm is proposed to adaptively optimize key network hyperparameters. Finally, the proposed method was validated using data from an SMR simulation model. Experimental results demonstrate that the proposed MSCC-CAE achieves a fault detection accuracy of 98.21%, outperforming CNN and conventional CAE models by 15.17 and 12.04 percentage points, respectively. The localization accuracy reaches 97.12%. These results verify the effectiveness and superiority of the proposed framework for intelligent sensor fault detection in the SMR system. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

Item description information plays a crucial role in helping users understand the basic situation of an item and is also vital auxiliary information in recommendation systems. Traditional methods obtain this data through platform backend data or web scraping techniques, but these data are often static, relatively fixed, and insufficiently descriptive. In recent years, large language models (LLMs) like generative pre-trained transformer (GPT) have become powerful tools in natural language processing, bringing new hope for LLM-based recommendations. However, does the text information generated by large language models help improve recommendation accuracy? How can the information produced by generative artificial intelligence be integrated with existing multi-source heterogeneous information? In this paper, we propose a novel deep hybrid recommendation method for multimodal information integrating content generated by large language models (DML). We first explore the use of large language models to generate detailed descriptive information about movies. Next, we perform a weighted fusion of the generated text information with existing movie category information and user demographic data, among other multi-source heterogeneous information. Finally, we use the fused information to predict movie ratings. The results indicate that the multimodal information deep hybrid recommendation method, which integrates content generated by large language models, provides substantial evidence of superior performance relative to existing baseline models. Full article

Heavy-duty vehicles (HDVs) are major greenhouse gas emitters, and liquefied natural gas (LNG)-powered HDVs have emerged as a promising low-carbon alternative. However, their real-world emission performance and mitigation potential remain insufficiently studied, necessitating the characterization of LNG container trucks’ on-road CO₂ emissions via advanced sensing technologies. To characterize HDVs’ emission characteristics, real-driving emissions from China VI LNG and diesel-powered container trucks were measured employing portable emissions measurement systems (PEMS). The results reveal that high CO₂ emissions predominantly occur during low- to medium-speed acceleration and at speeds above 40 km/h with an acceleration exceeding 0.3 m/s² on highways, whereas emissions on port roads are more dispersed. A third-degree polynomial function fits emissions well with vehicle-specific power (VSP). Engine parameters mainly influence CO₂ emissions for LNG trucks, while VSP and acceleration significantly impact diesel trucks. The Random Forest model achieves superior prediction accuracy, particularly in highway scenarios, and significantly better CO₂ forecasting for LNG-powered trucks. These findings validate the effectiveness of PEMS-based sensing in characterizing low-carbon HDVs’ real-world emissions. The integration of multi-source sensor data and machine learning also provides a reference for intelligent sensing in transportation environmental monitoring. Full article

An improved Sinh Cosh optimizer (ISCHO) is proposed to resolve load frequency control (LFC) tasks. The original Sinh Cosh optimizer (SCHO) employs a fixed iteration-based switching function to balance exploration and exploitation, which lacks awareness of search dynamics and leads to inefficient optimization. Therefore, this paper proposes a “first grabbing then washing” strategy to dynamically balance exploration and development. The proposed ISCHO technique is tested on 13 benchmark functions and compared with Particle Swarm Optimization, Sine Cosine Algorithm, and Grey Wolf Optimizer, demonstrating superior optimization performance. Furthermore, a new controller based on the two-degree-of freedom PID controller (2DOF-PID), the two-degree-of freedom with double integral feedback PID controller (2DOF-PIDF-II), is proposed. A two-area multi-source interconnected power system, incorporating thermal, hydraulic, wind, and solar generation units with nonlinearities (GRC and GDB), uncertainties, and load fluctuations, is employed to validate the proposed approach. Quantitative results under step load perturbation demonstrate that the ISCHO-optimized 2DOF-PIDF-II controller significantly outperforms other methods. For area 1 frequency deviation, ISCHO reduces the maximum overshoot by 38.37%, 19.09%, and 21.48% compared to PSO, SCA, and SCHO. For tie-line power deviation, maximum overshoot is reduced by 53.00% compared to PSO. These results confirm that the proposed ISCHO-tuned 2DOF-PIDF-II controller substantially enhances system frequency stability under various operating conditions. Full article

The frequency of extreme weather events has become higher, and electricity consumption has also become more complex. These changes increase the risk of overload in distribution transformers (DTs), and this risk threatens the stability and reliability of the power grid. Existing methods have significant limitations. Traditional static threshold methods (based on DGA gas ratios and electrical signal thresholds) fail to consider temporal changes and complex links between factors, while modern machine learning models lack cause–effect relationships over time and clear ways to describe uncertainty. With such motivations, this paper proposes a causal-enhanced hybrid framework, which combines Long Short-Term Memory (LSTM) networks and Random Forest (RF) algorithms. The framework uses causal Seasonal Trend decomposition using Loess (STL) to reveal load patterns at different time scales. The mutual information index and spatiotemporal graph convolutional network (ST-GCN) are used to explore nonlinear relations and reveal how temperature affects load changes. The LSTM model captures time dependence in load series, and the Bayesian optimized Random Forest is used to solve the problem of data imbalance and quantify uncertainty. In addition, the framework constructs an early warning system that combines data from many sources in real time. Test results show that the proposed algorithm exhibits excellent performance in multi-source data environments. Full article

Driven by the global energy transition, mechanized construction—characterized by enhanced safety, efficiency, and quality—is becoming the mainstream approach in power grid development. Mechanization assessment serves as a critical tool for guiding and optimizing this process, yet current practices remain largely manual, resulting in inefficiency, time-consuming operations, and a lack of real-time insights, which severely limit its practical utility for dynamic project guidance. To address these challenges, this study proposes a novel framework that integrates semantic technology (i.e., ontology) and large language models (LLMs). The framework first constructs a semantic model of the power grid construction domain using ontology. An LLM is then employed to convert multi-source project data into structured ontological instances. Building on this, mechanization assessment criteria are formalized into machine-executable Semantic Web Rule Language (SWRL) rules, which enable automated reasoning and scoring through an ontological reasoner. Furthermore, the LLM is utilized to generate comprehensive and intelligible assessment reports based on the reasoning outputs. To validate the proposed method, 126 real-world project cases were applied to the system. The results demonstrate a 96% accuracy rate in mechanization assessment outcomes compared to expert evaluations. The approach facilitates an objective, standardized, and dynamic evaluation of construction mechanization levels, providing a foundation for intelligent and scalable management models in power grid construction. Full article

With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification of the governance complexity arising from multidimensional interacting pressures behind outage events. This creates a blind spot in both theoretical research and governance practice, hindering differentiated resilience decision-making. To address this gap, this study develops a four-dimensional evaluation framework of power outage governance complexity encompassing event attributes, external environment, internal system, and social impacts. Based on county-level outage data and multi-source auxiliary data in the United States from 2015 to 2024 and employing the XGBoost–SHAP interpretable machine learning approach, we construct the Power Outage Complexity Index (POCI) for all U.S. counties and systematically analyze its spatiotemporal evolution and core driving factors. The results show that outage governance complexity in the U.S. power grid exhibits a significant upward trend during 2015–2024, with an average annual growth rate of 1.84%. Spatially, significant positive autocorrelation is observed, and 146 high-complexity hotspot counties are identified, mainly clustered along the East and West Coasts, the Gulf Coast, and the Southwest. Driver analysis reveals that social impact and event attribute dimensions together account for nearly 90% of the variance in complexity, with cumulative outage exposure burden, outage frequency, and large-scale event ratio being the most critical drivers. Theoretically, this study extends power resilience research from an engineering-physical paradigm to a socio-technical governance paradigm and provides a reproducible methodological framework for assessing governance complexity in critical infrastructure systems. Practically, the POCI can serve as a governance diagnostic tool for the power industry and regulators, supporting resilience investment prioritization, emergency resource optimization, and differentiated governance strategy formulation. It also provides empirical evidence for safeguarding energy security in highly vulnerable communities and promoting energy resilience equity. Full article

Spring-energy-storage circuit breakers are critical switching devices in power systems, and their operating reliability directly affects the safety and stability of the grid. In practical operations of transmission equipment, contacts may experience degradation such as poor contact, overheating, etc., due to multiple factors, including contact arcing erosion, mechanical wear, oxidation aging, and reduced contact pressure. Developing contact-point health monitoring and assessment enables prognostic maintenance, improves power supply reliability, and reduces operation and maintenance costs. This paper surveys the related research on health monitoring technologies for contact-point state in spring-energy-storage circuit breakers, systematically sorting out the operating principles and application characteristics, vibration and acoustic emissions monitoring, as well as electrical and mechanical parameter monitoring. It further analyzes the key bottlenecks faced by current monitoring technologies in online measurement accuracy, anti-interference capability, and engineering applicability, and finally discusses the future development trends of intelligent monitoring integrated with artificial intelligence, multi-source data fusion, and digital twin technologies. The research results provide theoretical reference and practical guidance for the upgrading of contact-point state monitoring technologies and the construction of intelligent operation and maintenance systems for spring-energy-storage circuit breakers. Full article

This paper addresses the multi-source uncertainties faced by horizontal pumped storage-wind-solar (HWS) hybrid systems in the day-ahead market by proposing a hybrid stochastic-robust optimization model for bidding and scheduling. The model employs a scenario-based method to capture the randomness of wind and solar power output, utilizes Information Gap Decision Theory (IGDT) to handle the epistemic uncertainty in runoff inflow forecasting, and constructs a price-acceptance probability function based on historical statistics to characterize the market mechanism. By maximizing the system’s tolerable uncertainty immunity gap, the model co-optimizes generation schedules, pumped-storage operation, and market bids while ensuring that revenue under the worst-case inflow scenario does not fall below a predefined threshold. Simulation results based on an actual project in Hubei Province demonstrate that the proposed method effectively balances revenue and risk, showing significant advantages in both revenue stability and robustness compared to the system before retrofitting. This study provides practical decision-making support for hybrid systems with horizontal pumped storage participating in electricity markets. Full article

With the sharp increase in winter heating demand in northern China, the carbon emissions of combined heat and power (CHP) units remain high. This paper proposes a low-carbon optimal scheduling model for the system, considering the dynamic carbon-green certificate coupling and the multi-source energy supply of carbon capture and storage (CCS). Firstly, we analyze the thermal and electrical demand characteristics of the installed CCS and optimize its supply mode, and propose the corresponding low-carbon operation strategy for the CHP-CCS unit. Secondly, a dynamic coupling mechanism of carbon-green certificates with the acquisition volume of green certificates and the trading volume of carbon emission rights as the interaction medium should be constructed. The transmission effect of the historical trading volume on the current period should be achieved through dynamic prices, and a low-carbon economic scheduling model with the goal of minimizing operating costs should be established. Again, for the source-load uncertainty, by integrating the entropy weight method and the information gap decision theory, an IES optimization scheduling model based on the information gap decision theory method (IGDT) is established. Finally, through multi-scenario case simulation verification, the results confirmed that the proposed model can effectively improve the economy and low-carbon performance of the system. Full article