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Faulty Feeder Detection for Successive Single Phase-to-Ground Faults Based on Line Model Recognition and Correlation Comparison

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IEEE TRANSACTIONS ON POWER DELIVERY 2025年第2期40卷 750-763页

作者： Yuan, Jiawei Feng, Chuan Ji, Yueming Liu, Xintong Dong, Xuan Liu, Jun Jiao, Zaibin Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China State Grid Henan Elect Power Res Inst Zhengzhou 450000 Peoples R China

When a single phase-to-ground (SPG) fault occurs in distribution networks, the over-voltages in non-fault phases may cause insulation damage to entire networks, potentially leading to a secondary SPG fault in feeders. Existing detection methods fail to explore the underlying mechanisms of SPG faults, resulting in unsatisfactory detection performance in practical applications. Moreover, they cannot identify the second faulty feeder under successive SPG (SSPG) faults, thereby increasing the risks of bushfires and casualties. This paper proposes a faulty-feeder detection method for SSPG faults based on line model recognition and correlation comparison. Firstly, the fault characteristics under SPG and SSPG faults are analyzed, revealing that differences in the equivalent line models are essential for distinguishing between faulty and healthy feeders. Secondly, the derivative of zero-sequence voltage reflecting the capacitance characteristics is compared with the measured zero-sequence currents from the perspectives of similarity and distance. The first faulty feeder is detected using cosine similarity comparison, while the second faulty feeder is identified using Euclidean distance comparison. Thirdly, a comprehensive detection criterion is constructed based on the cooperation of the similarity comparison method and distance comparison method. The feasibility and applicability are verified using various simulation data, field tests, and practical data tests.

关键词： Circuit faults Capacitance Feature extraction Fault diagnosis signal processing algorithms Voltage measurement Grounding Coils Capacitance measurement Transient analysis Correlation comparison equivalent model faulty-feeder detection line model recognition successive single-phase-to-ground faults

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Machine Learning-Based Condition Monitoring for DC-Link Capacitors in AC/DC/AC Converters

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第4期72卷 4227-4237页

作者： Oruklu, Kerim Agalar, Sener Eskisehir Tech Univ Dept Elect & Elect Engn TR-26555 Eskisehir Turkiye

Power electronic converters play a pivotal role in diverse industries. Yet, reliability of these converters has been a critical concern since its early applications. Thus, this article proposes a machine learning (ML)-based condition monitoring (CM) system for dc-link capacitors (DLCs) in ac/dc/ac converters. The capacitance of DLC is chosen as the health indicator, and the power spectral density (PSD) of dc-link voltage ripple is proposed as a diagnostic tool. This technique offers resilience against measurement noise and eliminates the necessity for additional filtering techniques. Moreover, the proposed method does not require a high sampling frequency unlike the similar studies. Another notable advantage of this approach is that no additional circuitry is required, as it solely relies on the available signals. Moreover, the effectiveness of the ML-based CM system is evaluated through simulation results and an experimental validation on a laboratory prototype. The experimental results pointed out the high accuracy of the proposed CM system.

关键词： Capacitors Feature extraction signal processing algorithms Degradation Correlation Capacitance Training Condition monitoring (CM) dc-link capacitor (DLC) feature extraction machine learning (ML)

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Four-Channel Pulse Extraction and Matching Method for Loose Particle Localization on Sealed Electronic Equipment

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhai, Guofu Sun, Zhigang Wang, Guotao Zhang, Min Harbin Inst Technol Inst Reliabil Elect Apparat & Elect Harbin 150001 Peoples R China Heilongjiang Univ Sch Elect & Elect Engn Harbin 150080 Peoples R China

The existing loose particle localization research for sealed electronic equipment (SEE) focuses on the selection and optimization of classifiers, processing and optimization of datasets, and extraction of multidomain signal features, while neglecting the processing of four-channel loose particle signals at the source link, and there are many problems that need to be solved. Therefore, in this article, for the first time, the authors shifted their research focus to four-channel loose particle signals and proposed a suitable pulse extraction and matching method, which includes two stages. In the first stage, the authors newly proposed a two-stage variable multithreshold (TSVMT) pulse extraction algorithm. It takes the average energy and average zero-crossing rate (ZCR) of all sampling points in the signal as a reference, sets multiple thresholds according to the 3 sigma distribution criterion of normal distribution, performs prescreen on pulses through peak energy threshold, and accurately extracts independent pulses through endpoint energy threshold and endpoint ZCR threshold. This effectively solves the problems of unclear threshold setting rules, unstable threshold reference objects, ignoring pulses with smaller amplitudes, low reliability of pulse extraction and not considering continuous multiple pulses in the existing research. In the second stage, the authors newly proposed a threshold-judgment-search (TJS) pulse-matching algorithm. It takes the loose particle signal collected by the farthest acoustic emission (AE) sensor as the signal reference object, determines the starting time threshold based on the loose particle signal collected by the closest AE sensor, and searches for the pulse group that satisfies the starting time threshold. This effectively solves the problems of unstable signal reference objects, unreasonable signal reference objects, and unstable and unreliable pulse matching in the existing research. Feature extraction and dataset construction a

关键词： Location awareness Feature extraction Data mining Accuracy Sun signal processing algorithms Electronic equipment Background noise Training Search problems Loose particle localization machine learning pulse extraction pulse matching sealed electronic equipment (SEE)

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Angular-Domain signal processing Methods in MIMO-OTFS System

Angular-Domain Signal Processing Methods in MIMO-OTFS System

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IEEE Conference on Wireless Communications and Networking

作者： Qiqing Chang Jianfeng Wang Haiming Wang Lenovo Research Lenovo Group Ltd. Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Orthogonal time frequency space (OTFS) technology exhibits superior performance in high mobility environments. While integrating OTFS with Multiple-Input Multiple-Output (MIMO) technology can enhance spectral efficiency, significant challenges arise due to the complexity of channel conditions. In this paper, we propose signal processing methods for MIMO-OTFS according to different channel conditions. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS and extend it to MIMO-OTFS in the delay-Doppler-angle (DDA) domain. We formulate the signal relationships under three channel scenarios: ideal channel, channel dispersion and channel overlapping. Based on that, we propose the corresponding signal processing algorithms. Simulation results show that the proposed algorithms can improve system performance.

关键词： Time-frequency analysis Spectral efficiency System performance Simulation Space technology signal processing algorithms Transforms MIMO Dispersion Antennas

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FSRS: A Frequency Space Random Search Approach for Efficient Opaque-Box Adversarial Attacks

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Tsai, Min Han Huang, Qi Xian Sun, Hung Min Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu 300044 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300044 Taiwan

The imperative of ensuring machine learning safety has escalated concomitantly with its pervasive deployment. Among the myriad of potential threats, adversarial examples represent a particularly insidious form of attack, wherein malefactors introduce meticulously engineered perturbations to a model's input, thereby skewing its output. Recent developments have underscored the feasibility of such attacks even in the absence of intimate knowledge regarding the model's architecture, leveraging the model's output to iteratively refine these perturbations. Notwithstanding, the opacity inherent in opaque-box models-characterized by the unavailability of gradient information-necessitates a prodigious number of queries to mount a successful attack, thereby diminishing their efficacy. In response to these challenges, this study introduces the frequency space random search (FSRS) attack methodology. Distinct from conventional opaque-box strategies that operate within the spatial domain, the FSRS method innovatively manipulates the low-frequency domain, thereby curtailing the requisite search space. Furthermore, it harnesses the inherent efficiencies of random search algorithms to minimize the query footprint. Empirical validation of our approach reveals its superior performance in terms of query efficiency and success rate, all the while ensuring minimal perceptual distortion when benchmarked against contemporary opaque-box adversarial techniques.

关键词： Perturbation methods Frequency-domain analysis Discrete wavelet transforms Optimization signal processing algorithms Classification algorithms Sun Safety Computational modeling Machine learning algorithms Adversarial attacks deep learning discrete wavelet transform (DWT) random search

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Toward TMA-Based Transmissive RIS Transceiver Enabled Downlink Communication Networks: A Consensus-ADMM Approach

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第4期73卷 2832-2846页

作者： Li, Zhendong Chen, Wen Qin, Haoran Wu, Qingqing Zhu, Xusheng Zhang, Ziheng Li, Jun Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver. Specifically, we elaborate the downlink communication scheme using time-modulated array (TMA) technology, which enables high order modulation and multi-stream beamforming. Then, an optimization problem is formulated to maximize the minimum signal-to-interference-plus-noise ratio (SINR) with user fairness, which takes into account the constraint of the maximum available power for each transmissive element. Due to the non-convex nature of the formulated problem, finding optimal solution is challenging. To mitigate the complexity, we propose a linear-complexity beamforming algorithm based on consensus alternating direction method of multipliers (ADMM). Specifically, by introducing a set of auxiliary variables, the problem can be decomposed into multiple sub-problems that are amenable to parallel computation, where the each sub-problem can yield closed-form expressions, bringing a significant reduction in the computational complexity. The overall problem achieves convergence by iteratively addressing these sub-problems in an alternating manner. Finally, the convergence of the proposed algorithm and the impact of various parameter configurations on the system performance are validated through numerical simulations.

关键词： Transceivers Downlink Array signal processing Convergence Costs Streams Antenna feeds 6G mobile communication Transmitting antennas signal processing algorithms Transmissive reconfigurable intelligent surface (RIS) transceiver time-modulated array (TMA) linear-complexity beamforming consensus alternating direction method of multipliers (ADMM)

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Adaptive Joint Estimation of Temporal Vertex and Edge signals

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2025年 11卷 215-229页

作者： Yan, Yi Xie, Tian Kuruoglu, Ercan E. Tsinghua Univ Inst Data & Informat Tsinghua Shenzhen Int Grad Sch Shenzhen 518071 Peoples R China Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90007 USA Meta Inc Menlo Pk CA 94025 USA

The adaptive estimation of coexisting temporal vertex (node) and edge signals on graphs is a critical task when a change in edge signals influences the temporal dynamics of the vertex signals. Existing Graph signal processing algorithms have extensively studied signals on graph vertices, and while recent advancements have started exploring signals on edges, a framework for systematically representing interactive time-varying signals across vertices, edges, and higher-order structures has yet to be fully realized. We propose an Adaptive Joint Vertex-Edge Estimation (AJVEE) algorithm for jointly estimating time-varying vertex and edge signals through a time-varying regression, incorporating both vertex signal filtering and edge signal filtering. Accompanying AJVEE is a newly proposed Adaptive Least Mean Square procedure based on the Hodge Laplacian (ALMS-Hodge), which is inspired by classical adaptive filters combining simplicial filtering and simplicial regression. AJVEE is able to operate jointly on the vertices and edges by merging two ALMS-Hodge specified on the vertices and edges into a unified formulation. A more generalized case extending AJVEE beyond the vertices and edges is being discussed. Experimenting on real-world traffic networks and population mobility networks, we have confirmed that our proposed AJVEE algorithm could accurately and jointly track time-varying vertex and edge signals on graphs.

关键词： Laplace equations Information processing Filters signal processing algorithms Image edge detection Adaptation models Imputation Heuristic algorithms Time series analysis Symbols Adaptive algorithms graph learning graph signal processing (GSP) simplicial complexes time series

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Fault Diagnosis of Wind Turbines Based on Improved Dynamic Network Marker

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IEEE ACCESS 2025年 13卷 2474-2485页

作者： Pan, Zesheng Fang, Ruiming Wei, Tingyu Shang, Rongyan Peng, Changqing Huaqiao Univ Sch Informat Sci & Engn Xiamen 361021 Peoples R China

With the rapid increase in the installed capacity of wind power in China, early warning and identification of defects in wind turbine units has become crucial for the healthy development of the wind power industry. This paper proposes a comprehensive fault warning method for the entire wind turbine unit that is distinct from most current methods that focus solely on partial components. It addresses the complex coupling between various monitoring data from the supervisory control and data acquisition system and the internal defects of the unit. Utilizing a sample covariance matrix and the dynamic network marker theory, the method integrates diverse data inputs to predict potential failures. Case validation reveals that this approach maintains robustness across different operating conditions, issues warnings approximately 6 hours and 40 minutes before faults occur, and effectively identifies fault types. This capability is beneficial for initiative maintenance and rational planning of maintenance schedules in wind turbines.

关键词： Covariance matrices Eigenvalues and eigenfunctions Training signal processing algorithms Real-time systems Predictive models Power system stability Monitoring Long short term memory Feature extraction Fault detection fault diagnosis network theory predictive maintenance renewable energy systems supervisory control and data acquisition wind power

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Unraveling the Viral Spread of Misinformation: Maximum-Likelihood Estimation and Starlike Tree Approximation in Markovian Spreading Models

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 446-461页

作者： Yu, Pei-Duo Tan, Chee Wei Chung Yuan Christian Univ Taoyuan City 320314 Taiwan Nanyang Technol Univ Singapore 639798 Singapore

Identifying the source of epidemic-like spread in networks is crucial for removing internet viruses or finding the source of rumors in online social networks. The challenge lies in tracing the source from a snapshot observation of infected nodes. How do we accurately pinpoint the source? Utilizing snapshot data, we apply a probabilistic approach, focusing on the graph boundary and the observed time, to detect sources via an effective maximum likelihood algorithm. A novel starlike tree approximation extends applicability to general graphs, demonstrating versatility. Unlike previous works that rely heavily on structural properties alone, our method also incorporates temporal data for more precise source detection. We highlight the utility of the Gamma function for analyzing the ratio of the likelihood being the source between nodes asymptotically. Comprehensive evaluations confirm algorithmic effectiveness in diverse network scenarios, advancing source detection in large-scale network analysis and information dissemination strategies.

关键词： Fake news Maximum likelihood estimation Social networking (online) Probabilistic logic signal processing algorithms Immune system Heuristic algorithms Focusing Approximation algorithms Organizations Continuous-time SI model graph boundary analysis misinformation spreading network immunization rumor source detection starlike tree approximation

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Algorithm Unrolling Network With Learnable Sparse Regularization for Interpretable Mechanical Anomaly Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第5期21卷 3786-3795页

作者： Chen, Xiyue Wang, Shibin Wang, Shi-ao Ding, Baoqing Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Sparse representation-based interpretable algorithm unrolling is one of promising techniques for mechanical anomaly detection. In order to enhance the learning and representation capabilities of the algorithm unrolling model, this article proposes a learnable sparse regularization network (LSR-Net). Instead of imposing explicit regularization constraints on the encoding and dictionary during modeling, two subnetworks are designed to learn prior information: $\mathbf{Net}_{\mathbf{X}}$ and $\mathbf{Net}_{\mathbf{D}}$. The model is solved using the half quadratic splitting algorithm, and further unrolls the process of iterative computation into the form of a network. The architecture for encoding learning is structured as input convex neural networks, ensuring LSR-Net can learn meaningful encoding priors. Through the analysis of simulated and experimental data, it has been demonstrated that LSR-Net has strong feature extraction and noise resistance capabilities, and the design of its prior learning architecture is both reasonable and effective. In addition, the visualization of LSR-Net's overall reconstruction and the reconstruction of different dictionary atoms allows for both global and local interpretation of the learning results, thereby providing post hoc interpretability to LSR-Net. The visualization results confirm that LSR-Net is capable of learning features that align with mechanical vibration characteristics.

关键词： Convolution Encoding Feature extraction Anomaly detection Dictionaries Convolutional codes Analytical models Predictive models Neural networks signal processing algorithms Algorithm unrolling anomaly detection convolutional dictionary learning (CDL) interpretable neural network

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