Search Results (473)

Reliable operation of public facilities depends on continuous power-quality (PQ) monitoring and timely triage of anomalies that can compromise voltage stability, equipment life, and safety. We developed an analytical method for a legislative complex that fuses harmonic-based health indices with event persistence and a transparent priority score to rank PQ alarms in near real time. Feeder-level interval and harmonic measurements were collected from the Legislative Building (LB) of a Philippine municipal complex. A rolling, outlier-robust PQ index was constructed, and disturbances were detected using an unsupervised Isolation Forest algorithm. Point anomalies were subsequently consolidated into multi-sample events. Each event was scored according to a severity × duration × criticality rule to support dispatch decisions. On a one-week dataset comprising 2009 five-minute samples across six channels, the method flagged 41 anomalous points (2.0% of samples), which were consolidated into seven events when a 30 min guard band was applied. One dominant disturbance, lasting 2.25 h and reaching a maximum index of 8.91, had the highest composite priority score (20.05), while shorter or less severe excursions received lower scores. The method yields operator-ready artifacts, including ranked tables and summary plots, while remaining simple, explainable, and consistent with established PQ guidance. This makes it suitable for incremental deployment in public-sector buildings where resources for advanced analytics are limited. Full article

The increasing penetration of inverter-based renewable energy resources, especially photovoltaic (PV) systems, has decreased the available system inertia and introduced challenges in maintaining stable grid-forming operation. This paper presents a grid-forming photovoltaic multilevel inverter (MLI) with a modified delta-connected cascaded H-bridge (CHB) multilevel configuration. The proposed system decreases the number of semiconductor switches and provides inherent voltage balancing, while also achieving high power quality, rendering it suitable for grid-forming applications. Each H-bridge cell is connected to an isolated Cúk converter to enable maximum power point tracking (MPPT) of distributed PV modules, allowing for flexible and modular DC-side integration. The proposed MLI operates as a virtual synchronous generator. A control scheme is proposed to attain grid-forming capability, hence providing stable voltage and frequency support. Moreover, a DC-link voltage regulation strategy is also developed to maintain the DC-link voltage at the reference voltage. A detailed mathematical model is developed to characterize the associated dynamics of the proposed MLI and the control system with a grid interface. The model is built in the SIMULINK environment, and the simulation results are presented under variations in solar radiation and grid voltage disturbances to exhibit the functionality of the proposed system and the effectiveness of the control scheme in providing a well-damped frequency response and stable generated voltage and currents. The results demonstrate stable frequency regulation with a settling time of approximately 0.3 s, and the output current exhibits low harmonic distortion, with a Total Harmonic Distortion (THD) of about 0.53%. Simulation results show stable operation and confirm that the proposed approach is a competitive solution for PV-based grid-forming applications. Full article

To address fault-period DC-link overvoltage, the reduction in grid-side active-power regulation margin caused by reactive-current-priority operation, and the double-frequency current fluctuation induced by negative-sequence components under asymmetrical grid faults in a flywheel-assisted permanent-magnet direct-drive wind power system, this paper proposes a coordinated low-voltage ride-through (LVRT) strategy based on DC-link-voltage-threshold partitioning. According to the DC-link voltage level, the operating process is divided into a normal regulation region, a grid-side saturation region, and a flywheel activation region, thereby enabling coordinated regulation between grid-side reactive-current support and flywheel-side active-power absorption. To improve transient smoothness, an anti-windup mechanism together with a bumpless transfer scheme is incorporated into the coordinated control process to suppress integrator saturation and mitigate mode-transition disturbances. In addition, a grid-side proportional–integral–vector resonant controller (PI-VRC) is introduced to improve the suppression of double-frequency current fluctuation under asymmetrical faults and enhance converter capacity utilization. Simulation results show that the proposed strategy can effectively restrain fault-period DC-link voltage rise, improve three-phase current symmetry and grid power quality, and strengthen transient reactive-power support, thereby enhancing the asymmetrical-fault LVRT capability of the system. Full article

Power quality disturbance signals must be continuously monitored, stored, and transmitted for effective analysis, protection, and system planning in modern power systems. The large volume of data generated during power quality monitoring necessitates efficient storage techniques. The sparse representation of power quality signals can significantly reduce memory requirements while preserving important signal characteristics. Since several techniques exist for obtaining sparse representations, it is important to identify the most suitable Sparse Signal Decomposition (SSD) technique for different power quality disturbances. This paper presents a comparative study of various SSD techniques, including Orthogonal Matching Pursuit (OMP), Matching Pursuit (MP), Least Squares–Orthogonal Matching Pursuit (LS-OMP), and Thresholding and Basis Pursuit (BP), along with diverse dictionaries for the representation of power quality disturbances such as sag, swell, transients, and harmonics. Mean Square Error (MSE) and the ratio between the actual signals and reconstructed signals (A/R ratio) are used to evaluate the accuracy, while computation time is considered to compare the computational speed of different techniques. Simulation studies are carried out in MATLAB to evaluate the effectiveness of the SSD techniques. From the simulation results, it is observed that OMP and LS-OMP provide accurate representations of power quality disturbance signals. For sag, swell, and transients, the impulse dictionary performs best with OMP, offering faster computation. However, for harmonics, OMP with DCT dictionary is found to be more effective. Full article

Background and Objectives: Overactive bladder (OAB) is frequently associated with impaired quality of life and sleep disturbances, particularly in women with refractory symptoms. Non-invasive neuromodulation targeting autonomic regulation has emerged as a potential therapeutic approach. The purpose of this pilot trial is to assess the efficacy and safety of NESA non-invasive neuromodulation in the treatment of patients with overactive bladder. Materials and Methods: Triple-blind, randomized, sham-controlled pilot trial. Women ≥ 18 years with refractory OAB were randomized to active NESA or sham using an identical protocol (10 sessions, 60 min, twice weekly). Outcomes were collected at baseline, after session 5, and after session 10. The primary outcome was mean daily micturitions, assessed using a 3-day voiding diary. Secondary outcomes included other diary variables, urinary questionnaires (ICIQ-UI SF, ICIQ-QoL, B-SAQ), and sleep measures (PSQI, ISI). Results: Per-protocol analysis included 43 women (NESA n = 24; sham n = 19). At session 10, mean daily micturitions decreased from 9.19 to 8.07 with NESA and increased from 10.56 to 11.03 with sham (between-group p = 0.043; d = −0.97). Sleep improved with NESA versus sham (PSQI p = 0.001; d = −0.63; ISI p = 0.001; d = −0.59). Between-group differences in urinary symptom questionnaires were not significant. No device-related adverse events occurred. Conclusions: In this pilot trial, NESA neuromodulation was safe and showed preliminary signals of benefit, particularly for mean daily frequency and sleep outcomes. However, given the exploratory design, small sample size, and per-protocol analysis, these findings should be interpreted cautiously and confirmed in larger adequately powered trials before clinical implementation. Full article

This article highlights the intrinsic limitations of existing standards, such as EN 50160 and its associated measurement techniques, when applied to the assessment of power quality in high-speed railway traction power supply networks. These networks, characterized by intermittent and non-linear loads, generate disturbances (harmonics, voltage unbalance) that are not always detected or correctly quantified by standardized aggregation methods, leading to an underestimation of the actual impacts and calling into question the credibility of compliance assessments. The study proposes a new evaluation methodology based on synchronizing measurements with train traffic and grouping data by events rather than by fixed aggregation periods. This approach enables a more accurate characterization of negative-sequence voltage unbalance, providing a reliable estimation of both the magnitude and duration of disturbances. Experimental observations from multiple journeys and aggregation scenarios provide quantitative evidence supporting the relevance of the proposed improvements, which will contribute to updating and implementing standards better adapted to the specific characteristics of intermittent networks such as railway traffic, thereby ensuring a reliable, credible, and reproducible power quality assessment. Full article

The growing use of electrochemical batteries in renewable energy systems has intensified the need for storage architectures that can sustain power delivery while limiting transient electrical stress and voltage instability challenges. This study addresses the research gap in experimentally establishing a physically interpretable framework that links battery-centered hybrid storage behavior at the DC bus to AC-side inverter performance under load and source disturbances. A laboratory-scale renewable microgrid integrating photovoltaic and wind generation, programmable load variation, inverter-based AC delivery, and hybrid battery–supercapacitor storage is experimentally implemented and evaluated against a battery-only baseline, supported by a unified analytical framework that quantifies how transient buffering improvements propagate through the power conversion chain. The results show that the hybrid configuration reduces DC-bus voltage droop from about 1.1 V to 0.6 V under heavy-load transitions, and from approximately 0.85 V to 0.44 V during source-side variability (e.g., photovoltaic and wind turbine variations). The hybrid system also improves AC-side behavior, yielding unified stabilization indices of 103.03% for the root-mean-square voltage and 79.51% for the peak-to-peak voltage. These findings demonstrate that the experimentally implemented lab-scale renewable microgrid with hybrid battery–supercapacitor storage provides an effective pathway for improving battery-supported microgrid stability, waveform quality, and transient resilience. Full article

Aiming at the issues of low recognition accuracy and high model computational complexity for power quality disturbances (PQDs) in strong-noise environments, this paper proposes a novel lightweight PQD-recognition method that integrates a hybrid architecture of variational mode decomposition (VMD), convolutional neural network (CNN), and transformer. Firstly, a hybrid optimization algorithm named the monkey–genetic hybrid optimization algorithm (MGHOA) is proposed to optimize VMD parameters for denoising disturbance signals, thereby enhancing recognition accuracy in noisy environments. Secondly, to fully extract disturbance signal features and reduce the computational complexity of the model, a lightweight CNN–transformer model is designed. Depthwise separable convolution (DSC) is employed to extract local features and the multi-head attention mechanism of transformer is utilized to mine the long-distance dependence and global features, thereby enhancing the feature representation. Thirdly, a multitask joint-learning method is proposed to collaboratively optimize classification accuracy and temporal localization tasks, enhancing the discrimination of similar disturbances. Additionally, a dual-pooling global feature fusion strategy is designed to further enhance the model’s ability to discriminate complex disturbances. Comparative experiments on 16 typical PQD types demonstrate that the proposed method achieves excellent performance in recognition accuracy, model robustness, and computational efficiency. The integration of the MGHOA–VMD module improves recognition accuracy by 1.08%, while the multitask joint-learning method contributes an additional 0.55% improvement. When achieving recognition accuracy comparable to complex models, the training time of the proposed method is 36.51% of that required by DeepCNN and merely 5.90% of that required by bidirectional long short-term memory (BiLSTM), with a 31.22% reduction in parameter scale. This work provides a novel solution for intelligent power quality disturbance recognition. Full article

Emergency locator transmitters (ELTs) are key safety systems for post-crash aircraft localization and search-and-rescue operations. In more electric aircraft (MEA), however, their design and operation are increasingly influenced by complex electrical architectures, tighter equipment integration, and more demanding electromagnetic environments. This paper presents a narrative literature review of ELT technology from a MEA-oriented perspective. A practice-oriented narrative approach is adopted, examining ELTs through a dual lens: the evolution of the search and rescue (SAR) ecosystem and the progressive electrification of aircraft systems. The review addresses ELT fundamentals, classifications, operating principles, and interaction with the Cospas-Sarsat infrastructure, and examines the transition from legacy analog beacons to modern 406 MHz digital systems incorporating GNSS positioning, MEOSAR capabilities, second-generation beacon functionalities, and distress tracking features. Particular attention is given to integration challenges in MEA platforms, including autonomous energy supply, battery endurance, power quality disturbances, electromagnetic compatibility, installation robustness, antenna survivability, and certification constraints. The analysis highlights that ELT performance in MEA depends not only on the beacon itself, but also on the coupled interaction among device design, installation conditions, and the electrical environment. Finally, the review outlines research priorities for next-generation ELTs, including improved survivability assessment, energy-aware architectures, integration strategies based on electromagnetic compatibility, and certification-ready solutions compatible with future aircraft platforms. Full article

Classifying power quality disturbances (PQDs) under strong noise conditions remains challenging for existing deep learning models. These models typically separate denoising from feature extraction, often rely on attention mechanisms that operate along only a single dimension, and tend to achieve high accuracy at the cost of high complexity, which limits their performance under low signal-to-noise ratio conditions and hinders practical deployment. To address these limitations, this paper proposes AD-PDAF-Net, which organically integrates three key mechanisms through a co-design strategy. Unlike conventional methods that depend on preprocessing, an adaptive soft thresholding denoising layer is embedded into a lightweight residual network to progressively suppress noise during feature extraction, thereby unifying denoising with feature learning. A parallel dual attention module independently refines features along the channel and temporal dimensions, then adaptively fuses them using learnable weights to capture both frequency domain and temporal characteristics of disturbances. The lightweight network entry replaces aggressive downsampling with small convolutions to preserve transient details, and a bidirectional long short-term memory network (BiLSTM) efficiently captures temporal dependencies. Evaluated on a dataset of 25 disturbance categories defined in IEEE Std 1159-2019, the model achieves a classification accuracy of 97.26% and a Kappa coefficient of 97.02% under 20 dB white Gaussian noise, along with an accuracy of 98.78% under mixed noise conditions. The model has only 0.36 million parameters and a computational cost of just 1.50 GFLOPS. Through this co-design, AD-PDAF-Net achieves both high noise robustness and high classification accuracy with minimal computational overhead, offering an effective solution for time series signal recognition in resource constrained environments. Full article

In three-phase PWM rectifiers, abrupt load changes and parameter variations challenge DC-bus voltage regulation and degrade the performance of conventional controllers. To ensure robust regulation under nonlinear and time-varying conditions, this study proposes a type-3 fuzzy logic controller (T3-FLC) for DC-bus voltage regulation. The T3-FLC enhances the conventional type-1 framework by employing a three-dimensional membership structure that captures both vertical and horizontal uncertainties in the fuzzy inference process. This structure improves adaptability and stability in the face of system disturbances. The proposed controller was compared with a conventional proportional-integral (PI) controller and a type-1 fuzzy logic controller (T1-FLC) under different operating conditions: constant reference, reference tracking, load variation, regenerative operation, and grid disturbances. Under reference tracking mode, it settles within approximately 12 ms for the largest reference step, with the overshoot kept below 0.3%, whereas the T1-FLC and PI controllers require noticeably longer settling times and exhibit higher overshoot. In regenerative operation, the T3-FLC maintains tight DC-bus regulation with recovery times of 10–12 ms and an overshoot of about 2.7%, outperforming the benchmark controllers. Power quality analysis further shows that the proposed controller maintains low input-current distortion, with THD approximately 5–13%, and a near-unity power factor across all scenarios. These results confirm the T3-FLC as an effective control strategy for power converters. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

The expansion and ongoing refinement of control solutions for three-phase microgrids are key enablers in the transition from conventional distribution networks to smart microgrids. By integrating distributed generation, a microgrid can operate in either grid-connected or island mode. One of the major technical challenges in microgrid operation is mitigating or eliminating phase power unbalances. Unbalanced single-phase loads, combined with unbalanced and intermittent single-phase generation, can produce adverse effects on both energy efficiency and power quality. Unlike conventional distribution networks, microgrids may exhibit bidirectional power flows, which can occur simultaneously on all phases or differ from phase to phase. This paper introduces new analytical expressions for sizing a balancing capacitive compensator (BCC) for three-phase four-wire systems and derives a simplified sizing algorithm. The approach is validated through a numerical study using a Matlab/Simulink model of a low-voltage three-phase microgrid with high penetration of single-phase loads and single-phase distributed sources. The BCC is installed at the point of common coupling (PCC) between the microgrid and the main grid. Three operating regimes (cases) of the microgrid were analyzed, considering three compensation scenarios (sub-cases) for each: 1—without compensation, 2—with balanced capacitive compensation (classical), and 3—with unbalanced capacitive compensation (with BCC). For each of the three regimes (cases), the use of the BCC determines, at the PCC, in addition to the cancellation of the reactive component of the positive sequence current, the cancellation of the negative- and zero-sequence currents. In other words, the BCC–microgrid assembly is seen from the main grid either as a perfectly balanced active power load or as a perfectly balanced active power source. Thus, the BCC prevents the propagation of the unbalance disturbance in the main grid; in the considered case study, this also results from the cancellation of the negative- and zero-sequence components of the phase voltages measured at the PCC. The results show that the load-balancing capability of the BCC can be extended to power-flow balancing in any network section, including cases where the phase power directions differ. Implemented as a BCC-type SVC or as an automatically adjustable variant (ABCC), the proposed unbalanced shunt capacitive compensation method is effective for mitigating or eliminating bidirectional phase power-flow unbalances. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

Dry-type electrical transformers are essential components in commercial, industrial, and residential power distribution systems, as they adapt voltage levels required by a broad range of load types. Although they are robustly constructed, they are exposed to adverse operational and environmental conditions such as dust, humidity, and electrical disturbances that may cause premature winding damage, such as inter-turn short circuits. This study focuses on the detection of inter-turn short-circuit faults in a 15 kVA commercial dry-type transformer, where a fault equivalent to 11.54% of short-circuited turns was induced in the tap changers. Axial, radial, and rotational leakage magnetic flux signals were captured using a low-cost, non-invasive triaxial Hall-effect magnetic flux sensor. During data processing, Fisher Score feature selection was applied to identify the most relevant indicators. Subsequently, feature extraction techniques, including Linear Discriminant Analysis, Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection, and Isometric Mapping, were evaluated. The technique that best preserved global and local data structures was selected using Trustworthiness, Spearman’s correlation, and Kruskal’s stress metrics. PCA was selected as the optimal technique based on these quality metrics, achieving the highest classification performance. The resulting subspace data were classified using support vector machines and applying K-fold cross-validation. The proposed system achieved classification accuracies above 95%, with high recall and F1-score values, for inter-turn fault detection in each winding, confirming its effectiveness for reliable inter-turn fault detection in each transformer winding. Full article