Search Results (246)

Radial distribution feeders are especially sensitive to reactive-power deficits, which increase technical losses, deteriorate voltage profiles, reduce energy efficiency, and indirectly raise the emissions associated with the energy required to supply those losses. In this context, this paper proposes a sustainability-oriented planning methodology for the location and sizing of capacitor banks in radial distribution feeders, aimed at jointly improving technical performance, economic viability, and sustainability-related energy benefits. The problem is formulated as a discrete multi-objective model and solved through a constructive Greedy heuristic combined with backward/forward sweep load-flow evaluation, considering commercially available capacitor sizes. The methodology is validated on the IEEE 34-bus feeder, a demanding benchmark that remains less frequently used than the IEEE 33- and 69-bus systems in recent capacitor-planning studies. Seven scenarios are analyzed, from the uncompensated base case to configurations with up to six capacitor banks. The results show that all compensated scenarios improve feeder performance, reducing active losses from 25.3327 kW to a minimum of 20.1468 kW, equivalent to a maximum reduction of 20.47%, and increasing the minimum nodal voltage from 0.95528 p.u. to 0.97038 p.u. From a purely financial perspective, the one-bank scenario yields the highest net present value (USD 16,358.86), whereas the two-bank scenario emerges as the most balanced solution within the evaluated set, with annual savings of USD 5432.29 and a net present value of USD 11,497.58. Overall, the results confirm that capacitor-bank planning should be addressed as a trade-off among electrical efficiency, voltage support, profitability, and sustainability-oriented benefits. The proposed framework provides a simple, reproducible, and interpretable planning tool for radial distribution feeders. Full article

This paper develops a probabilistic Available Transfer Capability (ATC) framework that quantifies export headroom for renewables across transmission-distribution interfaces under time-varying uncertainty. Static transmission reliability margins can unnecessarily curtail exports. A dynamic transmission reliability margin (TRM) is embedded within ATC using rolling window statistics and adaptive confidence factor scheduling to release capacity in calm periods and tighten margins during volatile transitions. Uncertainty is modeled as net nodal power imbalance variability from load and renewable deviations, together with stochastic thermal limit fluctuations. Correlated multivariate scenarios are generated via Latin Hypercube Sampling with Iman-Conover correlation preservation and propagated through full AC power flow analysis. Validation on the IEEE 39-bus system and New Zealand’s HVDC inter-island corridor recovers 93.31 MW of usable transfer capacity on the IEEE system relative to the pooled Monte Carlo P95 constant-margin baseline, with 78.11 MW attributable to rolling window volatility tracking and 15.20 MW to adaptive confidence factor scheduling, and 59.51 MW (+7.6%) on the New Zealand corridor relative to the corresponding pooled Monte Carlo P95 baseline, with the gain arising primarily from rolling window volatility tracking. Relative to a 95% one-sided reliability target, achieved coverage is 93.9% for IEEE and 91.8% for New Zealand, translating into increased export headroom and reduced curtailment. Full article

Background: Emergency triage systems using machine learning traditionally rely on structured tabular data (vital signs), creating a “contextual blind spot” that ignores diagnostic information embedded in unstructured clinical narratives. Hybrid AI models that fuse tabular and text data may improve predictive discrimination, but the magnitude and conditions under which fusion adds value remain unclear. Methods: Five databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library) were searched from 1 January 2015 to 15 December 2025. Eligible studies employed Hybrid AI models integrating structured and unstructured emergency department data with quantitative baseline comparisons. Twenty-five studies (N ≈ 4.8 million encounters) met inclusion criteria. We extracted marginal performance gains (ΔAUC), calibration metrics, and demographic reporting. Synthesis followed SWiM principles with subgroup meta-regression testing our novel “Complexity Gradient” hypothesis. Results: Hybrid models demonstrated superior discrimination compared to tabular baselines, with effect magnitude dependent on clinical task complexity. Low-complexity tasks (tachycardia prediction) showed minimal gains (median ΔAUC + 0.036, IQR: 0.02–0.05), while high-complexity tasks (hypoxia, sepsis) demonstrated substantial improvement (median ΔAUC + 0.111, IQR: 0.09–0.13). Meta-regression confirmed complexity significantly moderated effect size (R² = 0.42, p = 0.003). Only 12% (3/25) of studies reported calibration metrics (Brier scores: 0.089–0.142). Zero studies stratified performance by race/ethnicity; 88% (22/25) failed to report training data demographics. Discussion: The complexity gradient framework explains when multimodal fusion adds predictive value: tasks where diagnostic signal resides in narrative features (temporality, negation) rather than physiological measurements. However, systematic absence of calibration reporting and fairness auditing prevents clinical deployment. Seventy-two percent of studies had high risk of bias in the analysis domain due to retrospective designs without temporal validation. Conclusions: Hybrid triage models show promise for complex diagnostic tasks but require mandatory calibration reporting and demographic performance stratification before clinical implementation. We propose minimum reporting standards including Brier scores, race-stratified metrics, and temporal validation protocols. Full article

Reinforcement learning (RL) agents are increasingly deployed for voltage control in power distribution networks. However, their opaque decision-making creates a significant trust barrier, limiting their adoption in safety-sensitive operational settings. This paper presents XRL-LLM, a novel framework that generates natural language explanations for RL control decisions by combining game-theoretic feature attribution (KernelSHAP) with large language model (LLM) reasoning grounded in power systems domain knowledge. We deployed a Proximal Policy Optimization (PPO) agent on an IEEE 33-bus network to coordinate capacitor banks and on-load tap changers, successfully reducing voltage violations by 90.5% across diverse loading conditions. To make these decisions interpretable, KernelSHAP identifies the most influential state features. These features are then processed by a domain-context-engineered LLM prompt that explicitly encodes network topology, device specifications, and ANSI C84.1 voltage limits.Evaluated via G-Eval across 30 scenarios, XRL-LLM achieves an explanation quality score of 4.13/5. This represents a 33.7% improvement over template-based generation and a 67.9% improvement over raw SHAP outputs, delivering statistically significant gains in accuracy, actionability, and completeness ($p<0.001$, Cohen’s d values up to 4.07). Additionally, a physics-grounded counterfactual verification procedure, which perturbs the underlying power flow model, confirms a causal faithfulness of 0.81 under critical loading. Finally, five ablation studies yield three broader insights. First, structured domain context engineering produces synergistic quality gains that exceed any single knowledge component, demonstrating that prompt composition matters more than the choice of foundational model. Second, even an open source 8B-parameter model outperforms templates given the same prompt, confirming the framework’s backbone-agnostic value. Most importantly, counterfactual faithfulness increases alongside load severity, indicating that post hoc attributions are most reliable in the high-stakes regimes where trustworthy explanations matter most. Full article

This work proposes a hybrid GA–PSO framework for multi-objective distribution network reconfiguration aimed at reducing active power losses, improving voltage profile quality, and limiting switching effort under radiality and operating constraints. The methodology combines GA-based diversification of admissible radial topologies with PSO-based intensification around promising feasible solutions, while preserving an external archive of non-dominated solutions. Each candidate configuration is evaluated through AC power flow and validated by nodal voltage, branch loading, and radiality constraints. The proposal is tested on the IEEE 33-bus and IEEE 69-bus systems under three scenarios: S0 (base case), S1 (topology reconfiguration with $V_s=1.00$ p.u.), and S2 (S1 with substation-voltage adjustment). In the IEEE 33-bus feeder, losses decrease from 202.68 kW to 139.55 kW in S1 and to 129.70 kW in S2, while $V_{min}$ improves from 0.913 p.u. to 0.938 p.u. and 0.974 p.u. In the IEEE 69-bus feeder, losses decrease from 224.99 kW to 99.62 kW in S1 and from 224.99 kW to 96.83 kW in S2, confirming benchmark-consistent and operationally enhanced performance under coordinated voltage support. Full article

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization. Full article

The energy transition and electrification are increasing the use of power electronics in low-voltage networks, increasing losses and reducing service availability when harmonic currents are concentrated in the neutral. This study statistically evaluates power quality in a campus-type microgrid with a high proportion of nonlinear loads. The novelty of the work lies in combining field measurements, percentile-based neutral-current severity analysis, and standards-based comparative mitigation assessment in a low-voltage 3P4W campus microgrid. A campaign was carried out using a Fluke 1775 analyzer, recording trends, frequency, and events. Approximately 1900 events were recorded, mainly waveform deviations, interruptions, and rapid voltage changes. Voltage distortion was moderate, with a 95th percentile between 3.6% and 3.8%, while the neutral conductor concentrated the highest severity: neutral-current THD exceeded 220% in the 95th percentile and reached maximums above 700%, with 16.78 A in the 95th percentile at the measurement point. Based on IEC 61000-2-2 and IEEE 519, four mitigation measures were evaluated in DIgSILENT PowerFactory 2024 to estimate and reduce losses and heating: load balancing, detuned compensation, passive filtering, and active filtering. Active mitigation reduced the neutral harmonic component by 80% and the combined strategy decreased the neutral current at the measuring point by 78% (16.78 A to 3.69 A), with an estimated reduction in resistive losses of close to 95%. These results suggest sustainability benefits by reducing energy wasted as heat, extending the useful life of the infrastructure and improving operational resilience. Full article

Exposure to nature has been associated with benefits to human well-being, commonly evaluated using standardized psychological assessments and, more recently, neuroimaging modalities such as Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and functional Near-Infrared Spectroscopy (fNIRS). This systematic review and meta-analysis addresses the following questions. (1) How is the impact of nature on well-being studied using psychological and neuroimaging modalities and what does it reveal? (2) What are the challenges and opportunities for the deployment of wearable neuroimaging modalities to understand the impact of nature on the brain’s health and well-being? A search on PubMed, IEEE Xplore, and ClinicalTrials.gov (March 2024) identified 33 studies combining neuroimaging and psychological assessments during exposure to real, virtual or imagined natural environments. Studies were analyzed by tasks, populations, neuroimaging modality, psychological assessment, and methodological quality. Most studies were conducted in Asia (n = 23 or 70%). Healthy participants were the dominant target population (70%). In total, 61% of the studies were conducted in natural settings, while 39% used visual imagery. EEG was the most common modality (82%). STAI (n = 8) and POMS (n = 8) were the most common psychological assessments. Only seven studies included clinical populations. Two separate meta-analyses of nine studies with explicit experimental and control groups revealed a significant positive effect of nature exposure on psychological outcomes (Hedges’ g = 0.30; p = 0.0021), and a larger effect on neurophysiological outcomes (Hedges’ g = 0.43; p = 0.0004), both with moderate-to-high heterogeneity. Overall, exposure to nature was associated with reductions in negative emotions in clinical populations. In contrast, healthy populations showed a more balanced psychological response, with nature exposure being associated with both increases in positive emotions and reductions in negative emotions. Notably, 88% of the studies presented methodological weaknesses, highlighting key opportunities for future neuroengineering research on the neural and psychological effects of nature exposure. Full article

To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential locations for the oversized power network issue. This paper applies reinforcement learning (RL) to optimize for EV charging infrastructure at the university scale using real-world data, directly contributing to sustainable energy management by reducing grid burden and increasing renewable energy utilization. The RL-based charger aims to reduce the burden on the grid while increasing renewable energy utilization. This study investigated practical relevance in real-world systems, considering three demand scenarios: random, stochastic historical demand from Qatar University, and actual online data from Caltech University. Three RL algorithms—Deep Q-Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO)—are applied. While training, the historical stochastic data requires more tuning of the RL framework than the random demand, emphasizing the importance of realistic demand profiles. The performance of the RL approach depends on the type of demand. The results show that the proposed RL approach can efficiently mitigate the peak charging currents. For the Qatar University historical demand scenario, the PPO algorithm minimized the peak charging currents by 50% relative to uncontrolled charging (160 A to 80 A) and Model Predictive Control maintained the energy transfer capability at 99.710%. For the random demand type, the peak charging currents are minimized by 38.3% as compared to uncontrolled charging (128 A to 79 A), with a nominal reduction in energy transfer capability to 95.89%. Scalability is tested by integrating the model into the IEEE-33 bus network. Without solar integration, the proposed RL-based EV charging management model improves the voltage drop by 0.05 p.u., leading to reduction in the line losses by 17% as compared to the MPC benchmark method and by 32% as compared to the uncontrolled charging scheme. Further, the proposed RL approach leads to a 9% reduction in line current during peak hours in the IEEE-33 bus system. With solar integration into the IEEE-bus system, the proposed framework of the RL approach improved the sustainability of the charging infrastructures by enhancing solar energy utilization by 42.5%. These findings validate the applicability of the proposed model used for optimizing the sustainable EV charging infrastructure while managing the charging coordination problem. Full article

The intermittent nature of photovoltaic (PV) energy, especially under nonlinear and unbalanced loading situations, has made it more difficult to ensure steady operation as it is increasingly integrated into modern power systems. The Power Quality (PQ) issues cause severe degradation of both system performance and device lifetime. A novel Neural Power Quality Network (NeuPQ-Net) controlled Unified Power Quality Conditioner (UPQC) combined with a Quasi Z-Source Lift (QZSL) converter for PV applications is presented in this research as a novel solution for addressing these issues. The QZSL converter, which is controlled by a Maximum Power Point Tracking (MPPT) algorithm based on Perturb and Observe (P&O), increases the PV source voltage to the necessary DC-link level. A Zebra Optimisation Algorithm tuned PI (ZOA-PI) controller continually adjusts PI gains for quick and accurate regulation, ensuring steady DC-link voltage. Unlike conventional Synchronous Reference Frame (SRF) or Decoupled Double Synchronous Reference Frame (DDSRF)-based reference generation, the proposed NeuPQ-Net operates directly in the abc domain, eliminating Phase-Locked Loop (PLL) dependency and reducing computational complexity. Simulation and hardware prototype validations demonstrate that the proposed system achieves significant improvements in PQ indices, including reduced Total Harmonic Distortion (THD), faster response to transients, and enhanced voltage regulation, while complying with IEEE-519 standards. Full article

With the increasing penetration of distributed energy resources in power systems, the coupling between transmission and distribution networks has become increasingly complex. How to ensure short-term voltage stability (STVS) and maintain the supply–demand flexibility balance under complex transmission–distribution interactions and uncertain renewable generation has become a key challenge that must be addressed for coordinated transmission–distribution operation. To this end, this paper proposes a transmission–distribution coordinated optimal scheduling strategy that accounts for STVS and the supply–demand flexibility balance. First, the causes of short-term voltage instability were analyzed, and a time-domain simulation model of the power system was developed that incorporates the active voltage support capability of distribution networks. Second, an improved flexibility demand model was established based on the probability-box (p-box) method. Then, economic models for the transmission network and the distribution network were formulated, and a coordinated transmission–distribution operation model was constructed by considering both the short-term voltage instability risk and the supply–demand flexibility imbalance risk. Finally, a test system was built by connecting two modified IEEE 13-node feeders to buses 14 and 13 of the IEEE 14-bus system, and simulation studies were conducted. The results demonstrate that the proposed coordinated scheduling strategy can effectively reduce the risk of short-term voltage instability and ensure flexibility balance across the transmission and distribution networks. Full article

The automatic detection of foliar nutritional deficiencies through computer vision represents a promising alternative within precision agriculture practices, reducing dependence on laboratory analyses and the subjectivity associated with visual inspection. This systematic review maps and compares the application of machine learning (ML) and deep learning (DL) techniques to nutritional diagnosis across different crops, highlighting methodological trends, barriers to model adoption under field conditions, and existing research gaps. Following the PRISMA guidelines (PRISMA-P and PRISMA-2020), searches were conducted in the Scopus, IEEE Xplore, and Web of Science databases, using a defined time frame and explicit inclusion and exclusion criteria, resulting in 200 articles included (2012–2026; last search on 2 February 2026). The results indicate a predominance of DL-based approaches and RGB imagery, with applications concentrated in crops such as rice and in macronutrients, mainly nitrogen (N), phosphorus (P), and potassium (K), and report a marked increase in publications from 2020 onward. Although many studies report high performance, the evidence is largely derived from controlled environments and proprietary datasets, which limit model comparability, reproducibility, and generalization to real-world scenarios. Accordingly, the main research gaps include limited validation under field conditions, identified as the primary practical barrier; the underrepresentation of micronutrients and multiple-deficiency diagnosis; and the need for lightweight architectures suitable for deployment in mobile and edge-computing applications. It is concluded that ML and DL techniques offer promising alternatives for automated nutritional diagnosis; however, advances in data standardization, open-access datasets, and validation under real field conditions are essential for consolidating these technologies in practical applications. Full article

Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources and a fixed pilot structure, which degrade the performance and effectiveness of traditional estimation techniques, particularly in dynamic environments. Recent advances in deep learning offer significant potential for addressing these issues by improving estimation accuracy and modelling complex channel dynamics. Though deep learning-based methods introduce trade-offs in computational complexity and accuracy, these are crucial constraints in latency-sensitive V2V scenarios. This article presents a comprehensive review of deep learning-based channel estimation techniques, analysing methods for the IEEE 802.11p standard and critically examining their limitations in both classical and deep learning-based approaches. Additionally, the article highlights improvements introduced by IEEE 802.11bd, which features an enhanced pilot structure and advanced modulation schemes, providing a more robust framework for adaptive, efficient channel estimation. By identifying future research pathways that balance delay, complexity, and accuracy, an intelligent and effective transportation system can be established. Full article

The increasing penetration of inverter-interfaced photovoltaic (PV) generation in active distribution networks (ADNs) intensifies fast voltage violations and makes real-time Volt-VAR control (VVC) challenging, especially when each inverter has only partial and noisy measurements and communication is limited. Existing local droop-type strategies lack coordination, while fully centralized optimization/learning is often impractical for online deployment. To address these gaps, an attention-enhanced multi-agent deep reinforcement learning (MADRL) framework is developed for inverter-based VVC under the centralized training and decentralized execution (CTDE) paradigm. First, the voltage regulation problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP) to explicitly account for system stochasticity and temporal variability under partial observability. To solve this complex game, an attention-enhanced MADRL architecture is employed, where an agent-level attention mechanism is integrated into the centralized critic. Unlike traditional methods that treat all neighbor information equally, the proposed mechanism enables each inverter agent to dynamically prioritize and selectively focus on the most influential states from other agents, effectively capturing complex intercorrelations while enhancing training stability and learning efficiency. Operating under the CTDE paradigm, the framework realizes coordinated reactive power support using only local measurements, ensuring high scalability and practical implementability in communication-constrained environments. Simulations on the IEEE 33-bus system with six PV inverters show that the proposed method reduces the average voltage deviation on the test set from 0.0117 p.u. (droop control) and 0.0112 p.u. (MADDPG) to 0.0074 p.u., while maintaining millisecond-level execution time comparable to other MADRL baselines. Scalability tests with up to 12 agents further demonstrate robust performance of the proposed method under higher PV penetration. Full article

The increasing penetration of Distributed Energy Resources (DERs) in active distribution networks introduces significant voltage volatility. Traditional model-based control strategies often struggle to maintain voltage stability due to accurate parameter unavailability and time-varying topology. To address these challenges, this paper proposes a robust Measurement-Feedback Online Gradient Descent (MF-OGD) algorithm for real-time voltage regulation. Unlike conventional methods that rely on explicit network models, the proposed MF-OGD approach leverages real-time voltage measurements to correct gradient estimation errors, thereby implicitly compensating for both parametric mismatches and structural linearization inaccuracies. We provide rigorous theoretical guarantees for closed-loop stability and asymptotic tracking error under bounded disturbances. Furthermore, the framework is extended to a joint active–reactive power control scheme to ensure feasibility under severe operating conditions. Comprehensive simulations on the IEEE 33-bus and IEEE 69-bus standard test feeders validate the scalability and effectiveness of the proposed method. Numerical results demonstrate that the MF-OGD controller successfully maintains nodal voltages within the safety range, limiting the maximum voltage deviation to 0.022 p.u. even under 50% model parameter uncertainty. Additionally, the algorithm achieves a low tracking Root Mean Square Error (RMSE) of approximately 0.014 p.u. in the 69-bus system. Notably, the accumulated regret per node increases only marginally (from 0.032 to 0.038) as the network scale doubles, confirming the algorithm’s superior scalability and robustness compared to conventional open-loop baselines. Full article