Search Results (4,381)

Against the background of China’s dual carbon goals, high-renewable-power systems suffer severe resilience threats from destructive ice disasters, and existing recovery approaches fail to fully exploit multi-type flexible resources with unsatisfying computational efficiency. Targeting this gap, this work establishes a resilience enhancement framework for ice-affected power grids. This model quantifies line failure probability considering time-varying ice thickness and wind load, generates representative fault scenarios via sequential Monte Carlo and K-means clustering, and innovatively incorporates mobile energy storage systems (MESSs) and low-temperature-corrected PEM electrolyzers into coordinated post-fault dispatch; an improved parrot optimization (PO) algorithm with Chebyshev chaos, random mutation and adaptive t-distribution is designed to boost solving efficiency. Tested on the IEEE 39-bus system, the proposed method reduces average load shedding to 3.7% and raises renewable accommodation to 95.6%, outperforming fixed energy storage and literature-based strategies by cutting load curtailment by 45.6% and 30.2% respectively, while multi-condition sensitivity analyses validate its stable applicability under varying disaster intensity and renewable penetration. This coordinated scheduling strategy supplies feasible technical support for practical anti-icing resilience promotion of new-type power grids. Full article

The large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot (IPL) management separately, no unified framework exists that simultaneously optimizes all three flexibility tools. This research therefore aims to develop a coordinated scheduling framework that minimizes both energy losses and voltage deviations over a 24 h horizon. For solving the mathematical formulation, an Enhanced Grey Wolf Optimizer (EGWO) is developed using the concepts of dynamic neighborhood influence and self-adaptive convergence factor to prevent the issue of premature convergence and dynamic balancing of the algorithm during the search process. Simulation results on the IEEE 33-bus system across five scenarios quantify the benefits of each control layer. DSR alone reduces daily energy loss by 30.41%. Photovoltaic scheduling alone reduces loss by 15.40%. When combined, PV scheduling and DSR achieve a 38.29% loss reduction, demonstrating strong synergy. Full integration including IPL further improves voltage deviation by 40.26% compared to the base case, while maintaining loss reduction at 36.20%. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

The application of Internet of Things (IoT) technology to Digital Twin (DT) in smart manufacturing has opened significant opportunities for real-time monitoring, predictive maintenance, and closed-loop control; however, the inherent latency that exists in these architectures (the temporal gap between a physical event and its reflection in a digital model) remains one of the most significant and least systematically understood barriers to fulfill its full potential. This paper aims to propose a formal four-layer taxonomy of latency sources in IoT-based Digital Twin systems for smart manufacturing and to review the current approaches and tools that are available for their measurement. The PRISMA protocol has been used to perform a systematic literature review, where 58 primary survey studies published between 2020 and 2026 were extracted from IEEE Xplore, Elsevier Scopus, Google Scholar and arXiv, with all the studies being coded along six dimensions (architectural layer, application domain, latency metrics reported, evaluation methodology, quantitative impact, and enabling technologies). The proposed taxonomy presents 28 different types of latencies under four layers: (L1) network, (L2) compute, (L3) data, and (L4) end-to-end (E2E), whose magnitudes vary from 0.1 ms for local network propagation to tail latencies above 500 ms in production (P99). Three categories and three cross-layer interaction patterns are formalized here and are absent from prior partial taxonomies. Among the most promising results is the finding that several high-impact interventions require no infrastructure investment: a protocol migration from Modbus to WebSocket reduces telemetry latency by 32%, while Age of Information-aware synchronization and clock drift correction deliver substantial data layer gains through software updates alone, yet remain underutilized. The review identifies a systematic under-reporting of tail-latency percentiles across the corpus, the lack of a cross-protocol jitter benchmark, and a predominance of simulation-based evaluation over real-hardware measurement. The systematic review contributions of this paper (the formal four-layer taxonomy, the proportional metric audit across the 58 papers, and the formalization of three cross-layer interaction patterns) are derived from cross-corpus analysis. The investigation also identifies three open research directions (a standardized manufacturing IoT-DT benchmark, cross-layer joint optimization frameworks, and wireless TSN validation on real manufacturing testing grounds) that together form a well-organized and practical basis to advance both the science and the application of ultra-low-latency Digital Twin technology in the industrial field. Full article

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and $R^2$. The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests. Full article

This paper presents a bidirectional power flow control strategy for a grid-connected solar photovoltaic (PV)-based water pumping system employing a brushless DC (BLDC) motor drive. The proposed system enables continuous water pumping operation under varying solar irradiance conditions without the use of phase-current sensors while maintaining the motor at its rated operating speed. A single-phase voltage source converter (VSC) employs a unit vector template (UVT)-based control scheme that regulates bidirectional power flow between the utility grid and the dc-link, thereby supporting both grid-to-load and PV-to-grid power transfer. Excess photovoltaic energy can be exported to the utility grid during periods of reduced pumping demand, improving overall utilization of the available solar power. The voltage source inverter (VSI) driving the BLDC motor employs a PWM_ON_PWM switching scheme to reduce torque ripple while operating at fundamental frequency to minimize switching losses. The proposed system also incorporates maximum power point tracking (MPPT), power factor correction, and harmonic mitigation to improve power quality and ensure compliance with IEEE-519 requirements. The effectiveness of the proposed control strategy is evaluated through detailed MATLAB/Simulink R2023a simulations under various operating conditions. The simulation results demonstrate stable dc-link voltage regulation, bidirectional power flow capability, continuous pumping operation, and reduced torque ripple, highlighting the suitability of the proposed system for grid-interactive solar water pumping applications. Full article

Background: Near-infrared spectroscopy (NIRS)-based wearable devices offer non-invasive, continuous monitoring of muscle oxygenation, providing direct microvascular and metabolic information that complements indirect indices of intensity such as heart rate and accelerometry. Their clinical applicability in chronic non-communicable diseases (NCDs) remains under active development. Methods: A structured narrative review was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore (January 2010–January 2026) using pre-specified search strings combining NIRS, muscle oxygenation, SmO₂, StO₂, wearable, exercise intensity, ventilatory/lactate threshold, and individual chronic disease terms. Eligible studies addressed technical validation of wearable NIRS, NIRS-derived exercise intensity estimation, clinical applications in NCDs, or rehabilitation implementation. Evidence was synthesized thematically; quality of validation studies was appraised against AMSTAR-2-informed, COSMIN-informed, or Cochrane RoB-2 criteria. Results: Wearable continuous-wave NIRS shows acceptable concurrent validity with frequency-domain laboratory systems (r = 0.79; range 0.69–0.88; ±8% SmO₂ agreement in 95% of measurements) and good test–retest reliability for moderate-to-severe domains (ICC 0.72–0.91). NIRS-derived breakpoints align more reliably with the second ventilatory/lactate threshold (ICC = 0.80) than with the first (ICC = 0.53), constraining its use for prescribing lower-intensity domains. In chronic obstructive pulmonary disease, peripheral arterial disease, chronic respiratory failure and selected cardiovascular conditions, wearable NIRS detects disease-specific patterns of muscle deoxygenation and post-exercise reoxygenation that track responses to rehabilitation. Conclusions: Current evidence supports wearable NIRS as a complementary, intensity-aware monitoring tool—particularly for delineating the heavy/severe-intensity boundary and detecting peripheral metabolic limitations—rather than as a stand-alone replacement for ventilatory or lactate thresholds. Because much of the evidence derives from small, single-sex or athlete-only cohorts, these findings should be regarded as a promising basis requiring further validation in broader NCD populations. Implementation in NCDs requires standardized placement and calibration protocols, sex- and body composition-stratified reference values, motion-artifact mitigation, and adequately powered longitudinal trials in clinical populations. Full article

Modern power systems are becoming increasingly complex due to the rapid integration of renewable energy sources, the widespread use of nonlinear power-electronic devices, and the deployment of microgrids operating in parallel with conventional power grids. These evolving conditions intensify the occurrence of diverse and highly complex power quality disturbances (PQDs), demanding accurate and computationally efficient monitoring strategies. This paper presents a novel multi-stage hierarchical framework for PQD detection and classification, comprising an initial training stage with a dedicated 1D Convolutional Neural Network (1D-CNN), a transfer learning stage, and a subsequent fine-tuning stage. The proposed approach operates directly on raw voltage waveforms, eliminating the need for any signal preprocessing, as the CNN performs internal feature extraction. The framework is evaluated using a comprehensive dataset that includes synthetic signals, Matlab/Simulink (version R2022a) time-domain simulations, and real voltage sag events. Additionally, up to 29 types of disturbances, including complex multi-event combinations defined by the IEEE-1159 Standard, are generated using the PQ-SyDa toolbox. The proposed model achieves an F1-score of 97.8% using a three-cycle analysis window and further improves to 98.86% when five cycles are used. These results highlight the robustness and generalization capability of the proposed approach for the real-time PQD monitoring task in modern electrical networks. Full article

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem. Full article

Currently, there is a trend in the energy sector towards the application of edge computing techniques to facilitate active monitoring of distribution networks. The adoption of this technique is crucial for applications involving distributed monitoring systems that require real-time data processing with low latency. An edge computing environment ensures an adequate response to two time-level response requirements. One for events that could trigger a serious problem in the distribution network, and a less demanding one for the management of energy. This article justifies and analyzes an architecture specifically designed to provide an adequate response to the two levels of time demand that set the procedure followed for the monitoring, storage and local diagnosis of several photovoltaic plants located on the same distribution network, with the aim of studying their joint production. One of the main contributions is related to the expansion of the capabilities of Phasor Measurement Units (PMUs) to monitor solar radiation or energy production perimeters by sector. The second major contribution is to guarantee the quality of the measurements and low latency in communications, using as a reference the IEEE C37.118-2011 synchrophasor standard in cooperation with the Time Sensitive Networking (TSN) synchronization protocol that guarantees simultaneity in distributed measurements. In short, a procedure is sought that allows a real-time response with the use of computing techniques very close to the origin of the measurements, guaranteeing exhaustive control from the moment the capture begins until the parameters are stored in a time series database. Full article

Automatic gesture and movement recognition techniques are mainly used with adults for various purposes in public, clinical, and laboratory settings. Growing interest in this field has led to the increasing application of these methods in child behavioural analysis to serve different societal and educational functions. However, manual human annotation of behaviours remains the predominant method, and only a limited number of studies have explored the use of automatic recognition for children. This review aims to evaluate the rapidly developing techniques of automatic gesture and movement recognition that focus on child behaviour analysis across different settings and for different purposes. More specifically, it analyzes their purposes, target groups, settings, accuracy, and limitations, as well as the ethical issues and data privacy frameworks that should be considered in child-centred AI. Using a systematic review approach following the PRISMA guidelines, this study examines research published between 2021 and 2025 in four databases: Web of Science (WoS), Scopus, PubMed, and IEEE Xplore. From a total of 27 included studies, the findings reveal that automatic gesture and movement recognition is being applied across multiple fields, with consideration of children’s developmental needs. However, a critical gap in technical reporting was identified: fewer than half of the included studies (44%) provided accuracy metrics or clinical validity. Furthermore, evidence of robust ethical safeguards remains limited. To support children’s well-being, future studies must bridge the lab-to-field gap, prioritize natural research settings and enforce ethical and data protection measures. Full article

Component outage records in power systems are often limited, which makes it difficult to represent failure probabilities with deterministic point estimates. To address this issue, this paper proposes a reliability assessment framework that combines an improved Imprecise Dirichlet Model (IDM) with improved Transitional Markov Chain Monte Carlo (iTMCMC). The improved IDM introduces a sample-size-dependent hyperparameter to construct adaptive outage-probability intervals for different equipment categories. These interval probabilities are then propagated through iTMCMC to obtain interval-valued system reliability indices. In the sampling process, a reliability-oriented likelihood function is used to guide system-state exploration, and self-normalized weights are applied to maintain estimator consistency. A case study is conducted on a standard IEEE reliability test system. The results show that the improved IDM can provide adaptive component outage-probability intervals, while iTMCMC achieves more stable LOLP and EENS estimates than MC and MCMC. The interval propagation results further demonstrate that the proposed framework can transfer component-level probability uncertainty into system-level reliability-index intervals. The proposed method provides a practical tool for reliability assessment when component failure records are incomplete or insufficient. Full article

In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method. Full article

Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article