Search Results (90)

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit ($\mu$PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in $\mu$PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

Designing solid-state circuit breakers (SSCBs) involves a large discrete design space spanning MOSFET type, bypass configuration, and heatsink selection. This work formulates SSCB design as a multi-objective combinatorial optimisation problem that minimises conduction loss and material cost subject to electrothermal feasibility constraints. A validated electrothermal model was developed using experimentally measured R_DSon(T) data and thermal-impedance characterisation, allowing rapid and accurate evaluation of candidate configurations. Because the full design space exceeds one million combinations, five representative metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Grey Wolf Optimisation (GWO), Ant Colony Optimisation (ACO), and Gorilla Troops Optimisation (GTO), were benchmarked under an identical computational budget of 2000 evaluations. Sobol sequence initialisation was used to enhance search diversity. Each algorithm was executed 100 times, and its performance was quantitatively assessed using hypervolume, generational distance (GD), inverted generational distance (IGD), Hausdorff distance, overlapping-point score (OP), overall spread (OS), and distribution metrics (DM). GA consistently produced the closest approximation to the true Pareto front obtained from brute-force enumeration, achieving superior accuracy, coverage, and robustness. GTO offered strong secondary performance, while PSO, GWO, and ACO delivered partial front reconstruction. The results demonstrate that metaheuristic optimisation, particularly GA, can reduce SSCB design time significantly while retaining high fidelity, offering a scalable and efficient framework for future power-electronics design tasks. Full article

With the penetration of renewable power generation (RPG) in the distribution network (DN), power quality issues caused by RPG fluctuations have become more prominent than ever, let alone the integration of new types of power loads like electrified trains and electric vehicles that are major harmonic sources. Traditional power quality enhancement approaches are mostly dedicated to the smoothing of RPG power output or active compensation of harmonics, but fail to incorporate both routines into one single power quality enhancement scheme. Out of this research motivation, this paper aims to propose a synergetic allocation scheme for the hybrid energy storage system (HESS) and the unified power quality conditioner (UPQC) to achieve superior power quality enhancement. Firstly, a novel comprehensive vulnerability index of the DN suited for the power quality issues is presented to reflect the key factors that may impact the bus voltage security. Afterwards, the capacity specifications of HESS and UPQC for power smoothing and load side harmonic compensation are deduced with variational mode decomposition and inverter capacity configurations. Subsequently, the synergetic allocation method of HESS and UPQC are proposed by formulating an optimization problem, with the former obtained capacity specifications acting as the main constraints. After that, a dynamic hourly network reconfiguration approach is proposed to enhance the vulnerability level of the DN by dynamically changing its topology, and ensuring better power quality with the optimally allocated HESS and UPQC. Finally, simulation tests and comparative studies are conducted to evaluate the effectiveness and performance of the proposed scheme by comparing with existing methods. The comparative study has shown that the proposed method can reduce bus voltage deviation by 2.63%; meanwhile, it can reduce the total harmonic distortion by 1.83%. Full article

To address the challenge of low modulation recognition accuracy for Low Probability of Intercept (LPI) radar waveforms under low Signal-to-Noise Ratio (SNR) conditions—a critical limitation in current radar signal processing research—this study proposes a novel recognition framework anchored in an improved EfficientNet model. First, to generate time–frequency images, the radar signals are initially subjected to time–frequency analysis using the Choi–Williams Distribution (CWD). Second, the Mobile Inverted Bottle-neck Convolution (MBConv) structure incorporates the Simple Attention Module (SimAM) to improve the network’s capacity to extract features from time–frequency images. Specifically, the original serial mechanism within the MBConv structure is replaced with a parallel convolution and attention approach, further optimizing feature extraction efficiency. Third, the network’s loss function is upgraded to Focal Loss. This modification aims to mitigate the issue of low recognition rates for specific radar signal types during training: by dynamically adjusting the loss weights of hard-to-recognize samples, it effectively improves the classification accuracy of challenging categories. Simulation experiments were conducted on 13 distinct types of LPI radar signals. The results demonstrate that the improved model validates the effectiveness of the proposed approach for LPI waveform modulation recognition, achieving an overall recognition accuracy of 96.48% on the test set. Full article

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

When faults occur in transmission lines of grid-forming PV systems, the LVRT control and virtual impedance function cause the fault characteristics of grid-forming inverters to differ significantly from those of synchronous generators, which deteriorates the performance of existing protection schemes. To address this issue, this paper analyzes the fault characteristics of PV transmission lines under grid-forming control objectives and the adaptability of traditional current differential protection. Subsequently, a novel pilot protection based on the Jensen–Shannon distance is proposed for transmission line of grid-forming PV systems. Initially, the post-fault current samples are modeled as discrete probability distributions. The Jensen–Shannon distance algorithm quantifies the similarity between the distributions on both line ends. Based on the calculated distance results, internal and external faults are distinguished, optimizing the performance of traditional waveform-similarity-based pilot protection. Simulation results verify that the proposed protection reliably identifies internal and external faults on the protected line. It demonstrates satisfactory performance across different fault resistances and fault types, and exhibits strong noise immunity and synchronization error tolerance. In addition, the proposed pilot protection is compared with the existing waveform-similarity-based protection schemes. Full article

With the increasing level of inverter-based resources (IBRs) in modern power systems, this paper presents a small-signal stability analysis for power systems comprising synchronous generators (SGs) and IBRs. Four types of inverter controls are considered: two grid-following (GFL) controls, with or without grid support functions; droop-based grid-forming (GFM) controls; and virtual oscillator control-based GFM. We also analyze the impact of STATCOM and synchronous condensers on system stability to assess their role in the energy mix transition. With the small-signal dynamic behavior of the major technologies modeled, this paper provides stringent stability assessments using the IEEE 39-bus benchmark system modified to simulate future power systems. The exhaustive test cases allow for (a) assessing the impacts of different types and controls of generation and supplementary grid assets, as well as system inertia and line impedance on grid stability, and (b) elucidating pathways for the stabilization of IBR-dominated power systems. The analysis also indicates that future power systems can be stabilized with only a fraction of the total generation as voltage sources without SGs or significant system inertia if they are well distributed. This study provides insights into future power system operations with a high level of IBRs that can also be used for planning and operation studies. Full article

Over the past decade, China’s air quality has improved significantly. To further mitigate the concentration levels of fine particulate matter (PM_2.5), this study analyzed the spatio-temporal evolution of PM_2.5 concentrations from 2012 to 2022. Furthermore, the study integrated the generalized additive model (GAM) and GeoDetector to investigate the main driving factors and explored the complex response relationships between these factors and PM_2.5 concentrations. The results showed the following: (1) The annual average concentration of PM_2.5 in China peaked in 2013. The annual reductions of PM_2.5 in each city ranged from 1.48 to 7.33 μg/m³. In each year, the PM_2.5 concentrations were always consistently higher in north and east China and lowest in northeast and southwest China. (2) In terms of spatial distribution, the North China Plain, the Middle and Lower Yangtze River Plain, and the Sichuan Basin exhibited the highest PM_2.5 concentration levels and showed high aggregation characteristics. (3) The GeoDetector analysis identified the concentrations of SO₂, NO₂, and CO and the meteorological conditions as important factors influencing the spatial differentiation of PM_2.5. The results of the GAM showed that the meteorological factors, such as temperature, atmospheric pressure, wind speed, and precipitation, generally had specific inflection points in their effects on the PM_2.5 concentration levels. The relationship of PM_2.5 with the gross domestic product and population density followed an inverted U shape. The PM_2.5 concentrations under the land use types of cropland, barren, impervious, and water were higher than others. The concentration of PM_2.5 decreased significantly under all land use types. Our work can be used as a strong basis for providing insights crucial for developing long-term pollution control strategies and promoting environmental sustainability. Full article

The Arabian–Nubian Shield (ANS) hosts numerous volcanogenic massive sulphide (VMS) deposits formed in submarine volcanic settings and enriched by hydrothermal processes, making it a critical region for mineral exploration due to the types of deposits it hosts and its geological complexity. The Wadi Bidah Mineral Belt (WBMB), located within the Arabian Shield, contains over 30 polymetallic VMS occurrences associated with an island arc system active between 950 and 800 million years ago. Despite its mineral potential, the WBMB still needs to be explored, with limited geophysical studies to support resource evaluation. This study focuses on the Hawiah area, a prominent VMS site within the WBMB, to delineate subsurface mineralization using transient electromagnetic (TEM) methods. TEM surveys were conducted to characterize the conductivity structure and identify potential zones of sulphide mineralization. Data were processed and inverted to generate 1D, 2D, and 3D resistivity models, providing critical insights into the depth, geometry, and continuity of the mineralized zones based on the final 3D resistivity distribution. The results revealed distinct conductive (very low resistivity) anomalies, correlating with known surface gossans and inferred sulphide-rich layers, and extended these features into the subsurface. The integration of TEM results with geological and geochemical data highlights the effectiveness of this approach in detecting and mapping concealed mineral deposits in complex geological environments. This study advances the understanding of VMS systems in the WBMB and demonstrates the potential of TEM surveys as a key tool for mineral exploration in the Arabian Shield. Full article

Solar electricity has become one of the most important renewable power sources due to rapid developments in the manufacturing of photovoltaic (PV) cells and power electronic techniques as well as the consciousness of environmental protection. In general, PV panels are connected to DC-DC converters and/or DC-AC inverters to implement the maximum power point tracking algorithm and to fulfill the load requirements. Thus, power conversion efficiency and power density need to be taken into consideration when designing PV systems. Three-port and partial power conversion technologies are proposed to improve the efficiency of a whole PV system and its power density. In this paper, three types of three-port converters (TPCs), including fully isolated, partly isolated, and non-isolated TPCs, are studied with detailed discussions of advantages, disadvantages, and comparisons. In addition, based on partial power conversion technologies, partial power two-port and three-port topologies are analyzed in detail. Their efficiency and power density can be further improved by the combination of three-port and partial power conversion technologies. Moreover, comparisons among seven different types of distributed PV systems are presented with their advantages and disadvantages. Compared to distributed PV systems without energy storage, distributed PV systems with hybridization of energy storage and with partial power regulation can use solar energy in a more efficient way. Full article

In light of the accelerated development of renewable energy, inverter-based distributed power supply (IIDG) is assuming an increasingly pivotal role in contemporary power systems. This paper investigates the impact of inverter-based distributed power access on current protection in distribution networks and proposes an optimization method. Firstly, the IIDG power model is introduced for the purpose of analyzing the impact of distributed power supply access to an urban distribution network on the current magnitude of the distribution network. Subsequently, the issue of protection sensitivity following IIDG access is examined in the context of time-limited current flow protection and fixed-time overcurrent protection, respectively. To address the issue of inadequate sensitivity, a refined enhancement strategy for the real-time monitoring of the line current and modifying the action setting values is put forth. Finally, the proposed protection methodology is validated through PSCAD simulation, thereby verifying the effectiveness of the proposed optimization and improvement solutions in the real-time monitoring of the line current and adjustment of the action setting values. Full article

In this paper, a signal processing method based on Mathematical Morphology (MM) is developed, designed to extract representative characteristics of signals that allow the identification and detection of various types of faults in distribution network feeders that incorporate distributed generation with inverter interfaces (IIDG). The goal is to improve the performance of the fault protection system, ensuring rapid, sensitive, and reliable detection. The fault detection method presented in this article employs a well-known signal processing filter, called the morphological median filter (MMF), applied to the current measured at the current transformer (CT) associated with the relay located at the head of a feeder in a medium-voltage distribution network with IIDG. The extracted characteristics will be used in future research to detect and classify events, such as short-circuit faults or operational manoeuvres, thus facilitating the implementation of protection strategies. Full article

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need for enhanced load modeling to mitigate planning and operational uncertainties. Using MATLAB/Simulink^®, we simulate four distinct study cases characterized by varying load types and levels of distributed generation (DG), particularly solar PV, under both balanced and unbalanced conditions. Our findings highlight the critical role of DG in influencing voltage stability, revealing that deviations in voltage and current during grid imbalances remain within acceptable limits. The study underscores the importance of DG-based inverters in maintaining grid stability through reactive power support and sets the stage for future research on microgrid simulations and battery storage integration to further enhance system stability and performance. Full article

The Pareto–Feller distribution has been widely used across various disciplines to model “heavy-tailed” phenomena, where extreme events such as high incomes or large losses are of interest. In this paper, we present a new bivariate distribution based on the Appell hypergeometric function with marginal Pareto–Feller distributions obtained from two independent gamma random variables. The proposed distribution has the beta prime marginal distributions as special case, which were obtained using a Kibble-type bivariate gamma distribution, and the stochastic representation was obtained by the quotient of a scale mixture of two gamma random variables. This result can be viewed as a generalization of the standard bivariate beta I (or inverted bivariate beta distribution). Moreover, the obtained bivariate density is based on two confluent hypergeometric functions. Then, we derive the probability distribution function, the cumulative distribution function, the moment-generating function, the characteristic function, the approximated differential entropy, and the approximated mutual information index. Based on numerical examples, the exact and approximated expressions are shown. Full article