Search Results (744)

The objective of the research presented in this paper was to develop methods for simulating the operating conditions of traction power supply systems (TPSSs) equipped with asynchronous generators (ASGs), which may be driven by wind or hydraulic turbines as prime movers, thereby significantly reducing train traction energy costs and lowering carbon monoxide emissions. Using phase-coordinate methods and the Fazonord AC-DC industrial software package, simulations were performed for a TPSS configuration comprising three traction substations (TSs) with ASGs connected to the 6 kV busbars. The results demonstrate that connecting the ASG reduces the maximum active power flow from the utility grid by 27%, decreases peak losses in the 220 kV primary supply line by 44%, and lowers voltage unbalance levels at the 220 kV busbars by 71–76%. Additionally, electromagnetic safety conditions along the 220 kV overhead lines feeding the substations are improved, and the temperature at the hottest points of the traction transformers is reduced. The developed ASG models, implemented using three controlled current sources, are universal and can be applied to TPSSs of various configurations and design layouts. Full article

Passive single-phase to three-phase conversion represents an attractive alternative for low-power applications, particularly in isolated systems and rural electrification scenarios where simplicity, robustness, and low cost are essential. However, these passive topologies inherently produce voltage unbalance whose magnitude strongly depends on load characteristics. This work analyzes a passive single-phase to three-phase converter based on reactive elements and a transformer, focusing on the limits of voltage unbalance mitigation through discrete transformer tap optimization. The study is conducted under steady-state sinusoidal conditions using phasor modeling and symmetrical component analysis. The voltage unbalance factor (VUF) is adopted as the primary optimization metric, while the current unbalance factor (IUF), neutral current, and converter losses are used as complementary performance indicators. Results indicate that transformer tap optimization can reduce voltage unbalance for specific load conditions, although low residual unbalance is achieved only near the nominal operating point. Higher residual unbalance is observed as the load becomes more inductive within the investigated power-factor range. The findings indicate that passive single-phase to three-phase conversion can be technically viable for low-power applications with relatively stable load conditions. However, applications requiring high power quality or dynamic regulation may benefit from active converter solutions based on power electronics. Full article

In practical photovoltaic grid-tied four-leg inverter systems (PV-GTFLIs), changes in radiation, temperature, and grid voltage magnitude cause significant disturbances, including DC-link voltage disturbance and power unbalance, which can impact the system’s dynamic responses, control performance, grid power quality, efficiency, and reliability. To deal with these problems, this article proposes an improved active disturbance rejection control (ADRC) methodology for optimizing DC-link voltage regulation in PV-GTFLIs. The proposed ADRC approach incorporates a nonlinear high-gain observer (NHGO) within the external DC-link voltage control loop to estimate and mitigate disturbances. The suggested ADRC approach adopts the NHGO instead of the traditional extended state observer due to its superior characteristics, which include excellent dynamic responses, rapid and accurate disturbance estimation and rejection, and enhanced resilience against measurement noise. Thus, it enhances the stability of the DC bus voltage, enhances the system’s dynamic responses, improves its steady-state performance, increases its ability to reject voltage disturbances, and improves the reliability of the PV-GTFLI, as well as reducing the cost and size. The effectiveness of the proposed ADRC approach based on the NHGO is confirmed through software-in-the-loop real-time validation tests, including changes in irradiation and PV cell temperature, sag in grid voltage amplitude, and internal uncertainties, using the OPAL real-time digital simulator. Full article

The ambitious roadmap for a sustainable transport system adopted by the European Commission (EC) by 2050 includes the deployment of an extensive Electric Vehicle Charging Stations (EVCSs) infrastructure, which introduces significant challenges for distribution power grids. High power demand, particularly from fast-charging systems, may lead to network overloading and voltage unbalance. In addition, recent measurement campaigns highlight substantial changes in grid impedance and the emergence of resonance phenomena, together with the injection and propagation of high-frequency conducted disturbances. These effects extend over a wide frequency range, up to several hundreds of kHz, causing degradation, aging and malfunction of network assets, in particular Power Line Communications. This paper provides a comprehensive and updated review of the impact of EVCSs on electrical grids, covering power flow, power quality, stability, and impedance-related interactions. Particular attention is given to the role of power-electronic converters, high-frequency emissions, and the associated challenges in measurement and standardization. The analysis highlights that EVCS integration fundamentally alters the nature of electrical loads, requiring new approaches for grid planning, monitoring, and regulation. The study identifies key research gaps and outlines future directions to ensure the reliable and sustainable integration of electromobility into modern power systems. Full article

This paper presents a reproducible MATLAB R2025b benchmark for evaluating uniform, adaptive, and event-triggered sampling under a compound power-quality disturbance in a nonlinear three-phase system. The benchmark combines voltage swell, harmonic distortion, a damped transient, low-frequency oscillation, and phase unbalance within a finite interval. Performance is assessed using phase-specific and aggregated three-phase indicators, including RMSE, maximum error, detection delay, reconstruction percentage, spectral deviation, critical-time error, symmetrical components, and voltage unbalance factor. To ensure methodological fairness, the strategies are compared using a common linear reconstruction method and an additional experiment with an equal sample budget. Robustness is evaluated for different disturbance severities and durations, as well as under 40 dB and 30 dB noise, using 30 Monte Carlo realizations. The Fault Detection Sensitivity Index (FDSI) is introduced as a benchmark-specific composite metric and examined through 1000 weight perturbations of $\pm 20\%$. Under the natural acquisition configuration, event-triggered sampling achieved the lowest three-phase RMSE (0.0302 p.u.), the highest reconstruction percentage (96.149%), and the shortest detection delay (7.75 ms). Under equal-budget conditions, uniform sampling provided the lowest RMSE, whereas event-triggered sampling retained the fastest temporal response. The complete FDSI ranking remained stable in 100% of the sensitivity trials. The proposed framework provides a traceable pre-validation tool for sampling-based monitoring in industrial networks, microgrids, and converter-dominated electrical systems. Full article

The rapid electrification of marine transportation has increased the number of hybrid marine microgrids with the addition of renewables and energy storage. The continuously varying propulsion loads, fluctuating sea states, and renewable intermittency introduce significant challenges in bidirectional power transfer and power quality enhancement in marine vessels. This work presents a power-oriented operational strategy for a hybrid Roll-on/Roll-off (Ro-Ro) ferry-based marine microgrid (FMG) integrating diesel generators (DGs), Solar Photovoltaic (PV) arrays, and battery energy storage systems as the primary power sources. The proposed FMG adopts a hybrid AC/DC bus configuration linked through a bidirectional voltage source interlinking converter (ILC). The ILC facilitates multiple functionalities, including effective load compensation, mitigation of Total Harmonic Distortion (THD), continuous power support through bidirectional energy exchange, maintenance of balanced sinusoidal currents, and unity power factor (UPF) operation, thereby providing an integrated solution for improved power quality and reliable microgrid performance. A supervisory control (SC) is devised to operate the FMG seamlessly under islanded modes depending on the availability of power sources. To achieve the above-mentioned objectives, a power-optimized Dual Power-based Instantaneous Power Theory (DP_IPT) is employed and it involves a Sequential Delay Signal Cancelation (SDSC)-based Phase-Locked Loop (PLL) for the effective extraction of sequence components, so as to address the unbalance and nonlinearities in an effective manner with reduced oscillations. The proposed control strategy reduces diesel generator utilization through the effective integration of Solar PV and battery support during anchoring operation. The integration of renewable energy sources substantially enhances clean energy utilization, resulting in the reduction of overall carbon emissions, accounting for a near-40% decrease in emissions compared with the conventional diesel generator (DG)-based operating mode. The proposed FMG and control framework are validated through the Hardware-in-the-Loop (HiL) approach employing an OPAL-RT (OP4512) real-time controller. The HiL investigations demonstrate the efficacious working of the proposed control in achieving less carbonized and enhanced power quality operation for next-generation electrified hybrid maritime microgrids. Full article

Infrared thermography enables non-contact monitoring of induction motor thermal behavior, but absolute temperature alone may not distinguish faults with similar surface heating. This paper presents a proof-of-concept case study on the explainable thermographic diagnosis of three-phase induction motors using transient thermal signatures. Two faults were imposed on the same Siemens 1LA2080-4AA10 squirrel-cage motor: loss of forced ventilation (hereafter, cooling failure) and a resistive-bank-induced phase unbalance condition denoted in the test bench as 50% phase unbalance. The approach combines motor-specific regions of interest, transient thermal descriptors, hot area expansion, first-order thermal modeling, healthy baseline residuals, and two physically motivated indices: the Cooling Failure Index (CFI) and Phase Unbalance Thermal Index (PUTI). Cooling failure was analyzed from radiometric CSV data, whereas phase unbalance was evaluated from color-mapped thermal video through scale-based temperature reconstruction and is therefore interpreted as an estimated thermal signature. For the baseline self-reference consistency check, the residual-based fault flag remained false. Cooling failure increased the maximum radiometric temperature from 77.2 °C to 91.6 °C, with 43,399 pixels above 80 °C. Phase unbalance showed a localized stator-dominated rise without hot area expansion above 80 °C in the reconstructed sequence. The rule-based layer assigned high CFI to cooling failure and high PUTI to phase unbalance, supporting explainable case-study-based discrimination while avoiding claims of general classifier validation. Full article

This paper proposes a dynamic intelligent method for voltage violation management in high-renewable-penetration distribution networks. The method employs a dual-agent architecture: DERMS_Agent coordinates task scheduling, data management, and computational resource allocation, while Solution_Agent performs three-phase unbalanced power flow calculation and MIQP-based voltage violation joint optimization. Four key technical contributions are presented. (i) An asymmetric nodal admittance matrix is developed to incorporate transformer tap-phase-shift and capacitor branches within a unified formulation. (ii) Five categories of analytical sensitivities are systematically derived, covering transformer tap, phase shift, and capacitor compensation effects for both voltage regulation and harmonic suppression. (iii) A three-parameter MIQP joint optimization model is constructed with voltage deviation minimization as the objective and three-phase unbalance and resonance avoidance as constraints. (iv) A two-stage hybrid solution strategy combining Ipopt continuous relaxation with Gurobi neighborhood enumeration is designed to achieve real-time solvability. Validation on a real 10 kV feeder with 91 transformer areas over 768 time sections (8 days) demonstrates a 95.6% voltage violation resolution rate within the first three polling cycles and an average single-section solution time of 0.83 s, satisfying the real-time requirements of 15 min operational control cycles. Full article

This paper presents a single-sensor solution for the detection and monitoring of power supply voltage unbalances in AC/DC/AC converters. By analyzing the DC-link input current, it is demonstrated that characteristic current pulse deformations directly indicate the presence and severity of grid voltage imbalances. Based on these pulse profile variations, a novel diagnostic metric termed the Current Ripple Voltage Unbalance Factor (CRVUF) is derived. The proposed methodology was comprehensively evaluated and validated through both simulation studies and experimental testing. The presented approach offers a computationally efficient, non-invasive alternative to conventional monitoring systems, effectively reducing the number of required voltage sensors from three to a single current sensor. Full article

Phase resistance unbalance in robotic joint drive systems can alter electromagnetic torque generation and degrade motion accuracy, but its early diagnosis is challenging because fault-related signatures are weak and coupled with operating dynamics. This study proposes a short-time Fourier transform (STFT)-based multi-scale attention ResNet for phase resistance-unbalance diagnosis using synchronized multi-sensor signals from a single industrial robotic joint. Controlled resistance-unbalance states were generated on an eRob70F100I-BM-18EN joint module by inserting 0.05 $\Omega$ and 0.1 $\Omega$ series resistors into one motor phase with a nominal single-phase resistance of 0.75 $\Omega$. Current, acceleration, rotational speed, and torque signals were segmented and converted into four-channel STFT log-amplitude maps. A modified ResNet18 backbone was integrated with feature pyramid network (FPN)-style multi-scale fusion and a convolutional block attention module (CBAM) to enhance discriminative time–frequency features. Under a window-level stratified split, the proposed model achieved 98.97% accuracy and 98.97% macro-F1, outperforming raw-signal, fast Fourier transform (FFT), wavelet, STFT-ResNet18, STFT-VGG11-BN, STFT-MobileNetV2, and STFT-ShuffleNetV2 baselines. Grouped validation was conducted using file-level, leave-one-speed-out, and leave-one-load-out splits to assess robustness under stricter data partitions. The proposed model achieved 91.75% macro-F1 under file-level splitting and average macro-F1 values of 89.77% and 84.22% under leave-one-speed-out and leave-one-load-out validation, respectively. Grad-CAM visualization further indicates that the model relies on non-uniform local time–frequency regions rather than uniformly using the entire spectrogram. These results demonstrate effective robotic-joint resistance-unbalance discrimination while revealing that unseen operating conditions, especially specific speed and load settings, remain challenging for robust cross-condition deployment. Full article

With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case. Full article

Predictive maintenance (PdM) is central to Industry 5.0 strategies for reducing unplanned downtime in rotating machinery. This work proposes and evaluates, as a proof of concept on a controlled single-machine testbed, a multimodal TinyML edge architecture for PdM designed to remain compatible across the application plane’s evolution toward sixth-generation (6G) networks. Three complementary modalities run local inference on commercial off-the-shelf smart sensor nodes—vibration, acoustic, and thermography—with an embedded gateway bridging per-modality decisions to a serverless cloud back-end. Using real vibration data from a controlled static-unbalance protocol, five anomaly-detection model variants, operating on ten frequency-independent time-domain features extracted from 6 s windows, are benchmarked on the actual Cortex-M4F target; the INT8-quantized fully connected autoencoder, scored by per-window reconstruction error, reaches F1 = 0.9807 with 254 µs inference latency and a 6056 B Flash footprint, well within the microcontroller budget. In a second acquisition session with the remounted sensor, the frozen model retains perfect fault recall, and a short per-installation healthy-baseline recalibration restores F1 = 0.975 without any weight retraining. The acoustic modality is classified in-sensor on log-Mel filterbank energies by the Syntiant NDP120 neural coprocessor, and the thermographic modality by a lightweight binary CNN on 96 × 96 px frames. A preliminary intra-session late-fusion analysis suggests that a logistic-regression meta-learner over the three modality confidence scores can improve on single-modality baselines when no single modality already saturates, motivating multimodal sensing primarily for robustness and redundancy. An end-to-end latency experiment shows that the cloud-uplink leg dominates the budget (79–88%), establishing edge-first inference as a necessary condition for 6G URLLC gains to be observable at the application level. All experiments are conducted over Wi-Fi and MQTT with no 5G or 6G radio, so 6G compatibility is presented as a forward-looking roadmap rather than a tested capability. Full article

Existing DG allocation studies commonly rely on static or balanced feeder assumptions, leaving two practical issues insufficiently addressed: the hourly mismatch between renewable outputs and feeder demands and diesel-backup phase-imbalance safety in unbalanced networks. This paper presents a safety-aware allocation framework for hybrid photovoltaic (PV) and wind turbine (WT) systems in unbalanced three-phase feeders. The methodology explicitly accounts for 24 h generation–load coordination and diesel backup operating limits through two post-load-flow indicators: load-following error (LFE), which measures the hourly mismatch between aggregate distributed generation (DG) outputs and a load-proportional target, and power unbalance ratio (PUR), which limits diesel-unit phase-power imbalance to 10% during dispatch. The constrained siting and sizing problem is solved using a genetic algorithm (GA) on the IEEE 37-bus feeder under realistic diurnal load, solar, and wind profiles. While an unconstrained allocation achieves 64.57% active-power loss reduction, it exceeds the adopted diesel PUR screening threshold. Enforcing the PUR constraint yields a feasible operating state with 60.87% loss reduction, retaining 94.27% of the unconstrained benefit. Robustness checks across 30 independent runs and GA/PSO/ACO benchmarking confirm that the adopted GA provides the lowest dispersion and highly repeatable feasible outcomes. The results show that the framework improves energy efficiency, voltage regulation, and daily coordination while satisfying the adopted diesel phase-power screening criterion under severe feeder asymmetry. Full article

High penetration of residential photovoltaic (PV) systems frequently causes voltage violations and three-phase unbalance in rural low-voltage distribution systems (LVDS). These issues directly result in large-scale PV curtailment and significantly increase the operating costs of the rural LVDS. To address these issues, this paper proposes an operational cost optimization model and a corresponding solution method for the rural LVDS, explicitly considering power quality constraints. The decision variables of the model include the three-phase active and reactive power outputs of the distributed PV inverters and the energy storage system (ESS). The optimization objective is to minimize the total electricity cost of the rural LVDS, incorporating the operational cost of the ESS, subject to power quality constraints such as voltage limits and three-phase unbalance margins. Given the non-convex nature of the original model, second-order cone relaxation (SOCR) is employed to reformulate it as a convex optimization problem, which can be solved efficiently and robustly to global optimality. Simulation results based on real-world case studies demonstrate the effectiveness of the proposed method in mitigating voltage violations, reducing three-phase unbalance, and enabling economical operation. Overall, the proposed approach facilitates the high-penetration integration of distributed PV in the rural LVDS. Full article

Power-switch open-circuit (OC) faults distort electric-vehicle (EV) inverter phase currents and require fast on-board diagnosis for fault-tolerant control. Trajectory-image methods encode the $\alpha$–$\beta$ current-vector trajectory as a binary image and classify it with a convolutional neural network (CNN); however, the baseline uses $6.46\times {10}^5$ parameters and $3.31\times {10}^7$ multiply–accumulate (MAC) operations per inference, which is costly for motor-control microcontrollers (MCUs). Here, each one-cycle trajectory is represented by a 36-dimensional normalized angular sector-energy vector and classified by a compact two-stage multilayer perceptron. Sector accumulation averages zero-mean measurement noise in the representation, without relying on noise-augmented training. The locating stage uses $1.58\times {10}^4$ parameters and $1.56\times {10}^4$ MACs per inference, $97.55\%$ and $99.95\%$ fewer than the baseline CNN; the complete pipeline runs on a TI F28379D in $0.52$ ms. On measured resistive-load currents, both methods reach $100\%$ accuracy from 40 to 20 dB, whereas the proposed method remains more accurate at 15 and 10 dB, including under $88\%$ phase-current unbalance. A supplementary balanced $RL$-load experiment preserves $100\%$ clean accuracy, confirming MCU-executable diagnosis under a lagging power-factor load for embedded EV inverter protection. Full article