Search Results (1,697)

Interlinking converters (ILCs) are critical interfaces for coordinating power exchange in hybrid ac/dc microgrid clusters. Practically, different microgrids have varying inertia capacities and load priorities, it is urgent to design flexible power exchange control of interlinking converters for priority-driven dynamic sharing. To achieve optimal inertia inter-support among microgrids, a decentralized optimal dynamic control of ILCs is proposed for priority-driven inertia sharing among microgrid clusters. Firstly, an inertia interaction optimization model is established, incorporating subgrid priority weights and inertia-support capacity. Secondly, the established optimization model is implemented in a decentralized manner by deriving a local ILC control law from the optimality condition. Furthermore, a quantitative analytical framework based on a whole equivalent circuit model is constructed to reveal the impact of control parameters on key dynamic indicators. The proposed strategy features high scalability and less-communication requirements of decentralized control, enabling global optimization of transient performance and priority support for critical loads. Finally, the proposed method is validated through five representative cases in a Hardware-in-the-loop (HIL) platform. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

The conventional model predictive control (MPC) experiences a tremendous number of switching state evaluations per control cycle when applied to multilevel converters, which makes it computationally impractical. To address this issue, this article proposes a computationally efficient MPC (EMPC) for the cascaded H-bridge (CHB) static synchronous compensator (STATCOM), which is enabled by the sorting of the H-bridge submodules upon their dc capacitor voltages, such that the candidate switching states are restricted to the scope in which the lower-voltage submodules are charged and the higher-voltage submodules are discharged. And therefore, the exponentially increasing switching states in the CHB-STATCOM can be dramatically reduced while the computational efficiency is greatly improved. In addition, prior to control implementation, a generic discrete-time prediction model with the incorporation of a zero-sequence component is established to merge the balanced and unbalanced scenarios into one framework, so as to address the issues related to either grid and/or load unbalances in the CHB-STATCOM for distribution grids. Both simulation and hardware-in-loop experimental studies are provided to verify the effectiveness of the EMPC strategy. Full article

Controller comparisons for grid-forming battery inverters are often confounded by simultaneous changes in the plant model, saturation law, measurement filtering, and disturbance envelope. This paper addresses that problem through a reproducible weak-grid benchmark and a switching-averaged EMT validation layer for a battery-backed PV microgrid. Droop, virtual synchronous machine (VSM), and power-synchronization control (PSC) are compared under identical plant data, load disturbance, grid-strength reduction, voltage sag, current limit, and metric-extraction rules. The benchmark reveals a consistent trade-off: VSM provides the best frequency moderation, droop provides the fastest post-fault restoration and the lowest implementation burden, and PSC provides the most balanced compromise across recovery, stability, EMT, and implementation metrics. The averaged EMT layer preserves the low-order restoration ordering and sharpens the waveform trade-off during the fault window. Additional analyses quantify the converter-angle excursions during the sag, clarify the reduced lag tolerance of VSM as the grid becomes weaker, and test the local robustness of the reported ranking against representative tuning perturbations. The resulting message is benchmark-specific but operationally useful: controller selection should follow the dominant project objective—frequency quality, restorative efficiency, or balanced performance—before controller-specific switching EMT, hardware-in-the-loop, and plant-level studies are launched. Full article

Aiming at the problems of the severe active–reactive power coupling, insufficient adaptive inertia–damping regulation, and degraded dynamic performance of virtual synchronous generators (VSGs) under the operating conditions of a weak grid, high resistance-to-reactance ratio, and large power angle, this paper proposes a cooperative control strategy that combines reactive power feedforward decoupling with adaptive damping–inertia regulation. First, a small-signal power model of the VSG is established, and a dynamic relative gain array is employed to quantitatively analyze the effects of the resistance-to-reactance ratio and power angle on power coupling characteristics, revealing that large power angles and high resistance-to-reactance ratios significantly aggravate active–reactive power coupling. Based on this analysis, a reactive-power-oriented feedforward decoupling strategy is designed to suppress the cross-coupling between reactive power and power angle while preserving the intrinsic inertia support characteristics of the active power loop. Eigenvalue migration analysis further demonstrates that the proposed reactive-power-oriented decoupling provides higher damping ratios and larger stability margins than conventional full active–reactive power decoupling. Furthermore, a deep deterministic policy gradient-based adaptive damping–inertia control method is developed by incorporating frequency deviation, power fluctuation, voltage deviation, and coupling degree into the state space, enabling the online coordinated optimization of virtual inertia and damping coefficients. The hardware-in-the-loop experimental results verify that the proposed strategy effectively suppresses active–reactive power coupling, reduces power overshoot and oscillation, enhances frequency support capability and dynamic response speed, and maintains superior stability under weak grid conditions. Sensitivity analysis under grid impedance estimation errors further confirms its strong robustness against parameter uncertainty, while tests under composite disturbance scenarios demonstrate excellent transient performance. The proposed strategy provides an effective solution for improving the grid-connected operation performance and adaptability of VSGs in low-inertia power systems. Full article

This paper proposes the Continuous Operational Feedback Loop (COFL) architecture, a fully localized, event-driven operational monitoring and response system for Direct-to-Consumer (D2C) commerce. The architecture integrates the n8n workflow engine with on-premise large language model (LLM) inference via the Ollama framework, forming a containerized stack deployable on commodity CPU-only edge hardware (~USD 1640). Using a multi-source dataset of 1800 records constructed from publicly available e-commerce corpora and evaluated with a silver-standard automated labeling protocol, empirical validation demonstrates an end-to-end latency of 3.22 s and a macro-F1 sentiment classification score of 0.836—representing 98.2% of the full-precision baseline and 94.0% of cloud GPT-4o API generation quality measured by ROUGE-L—at approximately 1/200th of the per-request inference cost. A systematic quantization ablation study across six model-quantization configurations establishes LLaMA 3 8B Q4_K_M as the Pareto-optimal selection for the target hardware. An Analytic Hierarchy Process (AHP) multi-criteria framework with criterion weights derived from published literature confirms the COFL implementation achieves a higher composite score than cloud API deployment under the stated evaluation assumptions. Failure mode and effects analysis (FMEA) is summarized to characterize system reliability under identified failure scenarios. Full article

While the Triton compiler has revolutionized GPU kernel development, its deployment on general-purpose CPUs struggles to fully utilize the underlying hardware capabilities. This is primarily due to the semantic gap between Triton’s SPMD execution model and CPU vector architectures, which leads to suboptimal utilization of vector units during complex memory accesses. In this paper, we present a comprehensive compilation and runtime co-optimization framework for Triton-CPU, specifically targeting Vector Length Agnostic architectures (VLA) like ARM SVE. At the compiler level, we propose a novel semantic reconstruction and explicit base-offset decoupling strategy, enabling native VLA gather/scatter generation and eliminating scalar loop overheads. At the runtime level, we introduce a Machine Learning-driven thread scheduling model to optimally orchestrate the synergy between Thread-Level Parallelism and Vector-Level Parallelism. Extensive evaluations on an ARM-based multi-core processor demonstrate that our framework achieves up to a 2.0× throughput improvement for compute-bound GEMM operators (peaking at 346 GFLOPS), notably outperforming the hand-optimized OpenBLAS library by up to 1.54× at small-to-medium scales. Additionally, it delivers a 1.7× speedup for element-wise workloads. Furthermore, our optimizations saturate memory bandwidth (up to 55 GB/s) for memory-bound operators with zero compilation bloat, establishing a robust, high-performance foundation for deploying deep learning models on general-purpose CPUs. Full article

To address the potential oscillation instability issues of grid-forming (GFM) inverter systems integrated into grids with reactive power compensation devices, an impedance-based model of the grid-connected system is established. The impedance analysis reveals that the compensation capacitors alter the grid impedance characteristics, leading to impedance crossover points with insufficient phase margin in the mid-to-high frequency range, thereby inducing oscillations. To address this, a negative capacitive and virtual resistive loop-based composite control strategy is proposed. The grid-side capacitive effects can be neutralized through the virtual negative capacitance, and the system damping is enhanced by a virtual resistive loop to maintain stable operation under varying short-circuit ratios. Hardware-in-the-loop experiments validate that the proposed scheme maintains stable operation under various capacitance switching and grid strengths, thereby enhancing the robustness of the GFM inverter in complex distribution network environments. Full article

Quantum computing offers new possibilities for solving combinatorial optimization problems with rapidly growing search spaces. Among emerging hardware platforms, photonic quantum computers based on boson sampling provide a promising approach for sampling-based optimization methods. In this work, we investigate the application of the Binary Bosonic Solver, a hybrid quantum–classical algorithm designed for photonic quantum processors, to the binary portfolio optimization problem derived from the classical mean–variance framework. In addition to evaluating the feasibility of solving such problems on photonic quantum hardware, we analyze the behavior of the Binary Bosonic Solver algorithm under different architectural and optimization parameters, including interferometer loop configurations and gradient estimation methods. Benchmark instances are generated using historical financial market data, and experiments are performed both on a photonic quantum computer simulator and on the ORCA PT-1 photonic quantum processor installed at Poznan Supercomputing and Networking Center, with results compared to those obtained using a classical optimization algorithm. The results demonstrate that portfolio optimization can be successfully executed on current photonic quantum hardware and that the Binary Bosonic Solver algorithm consistently produces feasible and high-quality solutions, highlighting the practical potential of photonic quantum computing for combinatorial optimization problems. Full article

This paper presents an on-load programmable configuration of individual dual active bridge modules on a single-core transformer for wide voltage range and multi-port DC-DC power conversion. The mathematical models of power delivery and control transfer functions are presented for the proposed configurable converter. The universal control structure to implement the programmable configuration, control parameter programming, and closed-loop current regulation is presented. Simulation of the proposed converter and control is implemented in MATLAB/SIMULINK 2026A. A reduced-scale hardware prototype is implemented to validate simulation results. The performance of the converter in terms of feasible on-load switching of configurations and simultaneous regulation of multiple loads are compared to existing topologies, which demonstrated stable operation of proposed converter and control scheme over the investigated voltage range. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Under weak-grid conditions, grid-following (GFL) control of doubly fed induction generators (DFIGs) suffers from reduced stability margins, deteriorated dynamic performance, and intensified oscillations near the stability boundary. To address these issues, a pre-synchronized switching strategy between GFL and grid-forming (GFM) modes, triggered by locally measured operating variables, is proposed. Based on the GFL control model, the evolution of system dynamics with decreasing short-circuit ratio is analyzed, thereby elucidating how reduced grid strength progressively weakens robustness and disturbance rejection and eventually leads to instability. To characterize this deterioration, a set of normalized indices is constructed to quantify the oscillation levels of active power, phase-locked loop frequency, and point of common coupling voltage, enabling reliable identification of control-performance deterioration. A pre-synchronization scheme based on a virtual power closed loop is then developed, allowing the target mode to converge to the current operating point prior to takeover and enabling smooth bidirectional switching between GFL and GFM modes. Hardware-in-the-loop results demonstrate that the proposed strategy accurately detects GFL performance deterioration and effectively suppresses boundary oscillations while mitigating switching transients, thereby enhancing the adaptability of DFIGs to variations in grid strength. Full article

The growing complexity of software-defined automotive systems generates massive heterogeneous sensor and ECU data during real and virtual validation, and conventional rule-based analysis of such multivariate time series struggles under dynamic operating conditions, noise, and diverse fault scenarios. Deep learning-based anomaly detection has shown promising performance, yet existing approaches remain limited by static thresholds, insufficient robustness, and reduced interpretability. This study proposes an adaptive framework for intelligent fault detection in test recordings of automotive software systems (ASSs), integrating deep denoising autoencoders (DAEs), adaptive Gaussian thresholding, and explainable artificial intelligence (XAI) techniques. Four DAE architectures (ANN-, RNN-, GRU-, and LSTM-DAE) are systematically evaluated under different noise levels, system versions, and fault conditions, with detection thresholds that adapt dynamically to the statistical behavior of the reconstructed signals, thereby reducing false alarms under varying operating conditions. The framework was evaluated using real-world test recordings from IAV and Hardware-in-the-Loop (HIL)-based digital test drives, where ANN-DAE achieved the most robust detection performance, with F1-scores of 93.91% and 96.39% on the real and virtual test-drive data, respectively. Furthermore, the integration of XAI improved the transparency of anomaly interpretation at the signal level. Overall, the proposed framework shows strong potential for intelligent anomaly detection and quality assurance in safety-critical automotive systems. Full article

The seamless integration of real-time operational technology (OT) with big data architectures remains a critical bottleneck in developing robust robotic Digital Twins. Furthermore, evaluating stochastic motion planners strictly within pristine simulations obscures vital real-world challenges such as sensor noise, communication latency, and mechanical stress. This study presents a high-throughput, real-time Hardware-in-the-Loop (HIL) pipeline integrating ROS 2, Apache Kafka, and Functional Mock-up Units (FMUs). Using a UR10e manipulator in a constrained industrial environment, we conducted extensive physical benchmarking of 11 Open Motion Planning Library (OMPL) algorithms across 10 repetitions, generating a comprehensive dataset of 785,192 samples. The proposed IT/OT architecture achieved deterministic millisecond-level synchronization, bounding end-to-end communication latency between 0.09 and 15.51 ms. Physical executions revealed a macroscopic “topological divergence” between simulation and reality, with spatial deviations peaking at 457.65 mm due to real-world point-cloud noise. While algorithms like EST and KPIECE demonstrated optimal geometric efficiency (e.g., a mean path length of 14.57 m) and hardware-friendly dynamics, traditional planners like RRT generated severe inertial spikes of up to 100 N, demonstrating substantial unsuitability for continuous industrial deployment. The primary contribution is a methodologically novel, rigorously validated big data pipeline and the release of an open-source, 50 Hz multimodal dataset (spatial, temporal, and dynamic forces), bridging the sim-to-real gap and providing a foundational benchmark for future data-driven robotic applications. Full article

Inadequate management of municipal solid waste represents a critical challenge for the sustainability of modern cities, characterized by low citizen participation rates due to the lack of direct incentives. Unlike existing approaches that isolate hardware classification or fleet monitoring, this article presents RENOVA as a socio-technical closed-loop system based on the Internet of Things (IoT) and artificial intelligence (AI). This system integrates an IoT-enabled smart bin, a gamified mobile application for citizens, and an administrative web panel for merchant redemption, all interconnected via a REST API. The system employs computer vision through the GPT-4o (OpenAI, San Francisco, CA, USA) multimodal model for the automatic classification of recyclable materials (PET plastic and Aluminum) and integrates a gamified rewards program to incentivize citizen participation. The methodology follows an applied technological development approach under the agile Scrum framework. Prototype validation demonstrated successful real-time communication between the IoT device and the cloud platform, achieving classification accuracy exceeding 95% under controlled conditions. A diagnostic survey applied to a convenience sample of 51 participants revealed that 94.1% accepted the proposed gamification model, while user experience evaluation (n = 74; consisting primarily of university-affiliated individuals aged 15–24) yielded a mean overall satisfaction score of 4.77/5.0 (SD = 0.48), with 79.7% of participants assigning the maximum rating. These findings reflect stated user acceptance and behavioral intention under prototype conditions rather than observed long-term behavioral change, and should not be generalized to broader urban populations without further validation. The proposed solution directly contributes to Sustainable Development Goals 11 (Sustainable Cities) and 12 (Responsible Consumption), suggesting a potentially scalable framework. Full article