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fault detection and localization in an MT-VSC-HVDC Network via a Hybrid CNN-Transformer Model With a Multi-Head Attention Mechanism

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IEEE ACCESS 2025年 13卷 26365-26383页

作者： Mishra, Manohar Govind Singh, Jai Siksha O Anusandhan Deemed Be Univ Dept Elect & Elect Engn Bhubaneswar 751030 Odisha India Asian Inst Technol Sch Environm Resource & Dev Dept Energy & Climate Change Khlong Luang 12120 Pathum Thani Thailand

In this paper, a non-unit protection approach is suggested for recognizing fault events (internal and external) and their location in a High Voltage Direct Current (HVDC) system employing a deep learning (DL) model. This work utilizes a small sample size of DC current signals from one end of an HVDC-fed system, achieving a balance between data manageability and preserving vital information related to faults. The simulation of fault samples is carried out with the maximum possible operating scenarios, such as different fault resistances, locations, types of faults, and noisy conditions. Afterward, combined data with varying noise values is trained by the proposed DL model. The testing is performed under individual noise levels to evaluate model robustness and accuracy in real-time scenarios. In the DL framework, the work focuses on two core architectures, Convolutional Neural Networks (CNN) and Transformer models, and assesses their performance individually. Furthermore, it explores the hybrid model CNN-Transformer with a multi-head attention mechanism to capitalize on their combined strengths. Afterward, a detailed comparative result evaluation with other DL models is carried out to gauge robustness and accuracy in fault detection and localization. Comparative outcomes reveal that the Transformer models performed more remarkably in varying noise conditions, while recurrent models excelled in sequential data processing. The hybrid DL methods provided a balanced trade-off, improving fault detection and localization. The study utilizes several performance metrics such as accuracy, precision, recall, F1 score, MSE, RMSE, MAPE, and R2 errors to provide a comprehensive assessment. The fault detection without noise conditions attained an accuracy of 100%;however, there was a slight decrease (1.5%) in 20db noisy conditions. The proposed approach archives excellent performance in fault location tasks, such as MAPE:0.0308, MSE:1.6415, RMSE:1.2814, and R2:0.9982. The performan

关键词： HVDC transmission Protection Accuracy fault detection Data models Noise Transformers Feature extraction Voltage measurement Real-time systems fault detection and localization modeling of multi-terminal HVDC system internal and external faults application of deep learning models hybridization of CNN-Transformer model multi-head attention mechanism

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Shrinkage mamba relation network with out-of-distribution data augmentation for rotating machinery fault detection and localization under zero-faulty data

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Chen, Zuoyi Huang, Hong-Zhong Deng, Zhongwei Wu, Jun Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Peoples R China Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan Peoples R China

Data-driven fault detection (FD) or diagnosis methods are key technologies to ensure safe operation of rotating machinery. These methods rely on a requisite volume of fault data. However, acquiring fault data from rotating machinery is typically problematic and can be entirely unattainable. The critical challenge is to accurately detect and localize the fault states of rotating machinery under the absence of any fault data. Therefore, a newly shrinkage Mamba relation network (SMRN) with out-of-distribution data (OODD) augmentation is proposed for FD and localization in rotating machinery with zero-faulty data. Firstly, the corresponding sensors are arranged for the key detection locations on the rotating machinery. The data generator is referenced to generate OODD for the health data at each detection locations, assisting in mining of intrinsic state information from health data. Then, feature pairs are built in health data and OODD to reveal inter-state attribute relationships. Finally, the location of faults in rotating machinery is determined by evaluating the similarity between feature pairs at each detection location. The SMRN method effectiveness is verified by using self-built propulsion shaft system experiments and rolling bearing cases. The experimental results show the SMRN method can effectively detect and localize fault state of rotating machinery in multiple fault modes, compound fault scenarios, and variable operating conditions.

关键词： Rotating machinery fault detection and localization Zero-faulty data Relation network Out-of-distribution data

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fault detection and localization in Active Distribution Networks Using Optimally Placed Phasor Measurements Units

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第1期38卷 714-727页

作者： Conte, Francesco D'Agostino, Fabio Gabriele, Bruno Schiapparelli, Giacomo-Piero Silvestro, Federico Univ Genoa Dept Elect Elect & Telecomunicat Engn & Naval Arch I-16145 Genoa Italy Campus Biomed Univ Rome Engn Fac I-00128 Rome Italy

This paper introduces an algorithm able to detect and localize the occurrence of a fault in an Active Distribution Network, using the measurements collected by Phasor Measurement Units (PMUs). First, a basic algorithm that works under the assumption that all grid buses are equipped with a PMU is designed. Then, formal observability conditions that allow detection and localization with a reduced number of PMUs are provided. Based on these conditions, the algorithm is extended to perform correctly when not all network buses are monitored. Moreover, an Optimal Positioning Algorithm, always based on the observability conditions, is designed. This algorithm allows the user to customize the fault localization resolution. The approach is validated through simulations carried out on a benchmark active distribution network.

关键词： Distribution networks fault detection and localization phasor measurement units state estimation

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A Novel Open-Circuit fault detection and localization Scheme for Cascaded H-Bridge Stage of a Three-Stage Solid-State Transformer

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2021年第8期36卷 8713-8729页

作者： Gorla, Naga Brahmendra Yadav Kolluri, Sandeep Chai, Merlin Panda, Sanjib Kumar Natl Univ Singapore Dept Elect & Comp Engn Singapore 117581 Singapore

A three-stage solid-state transformer (SST) is a preferred replacement for line frequency transformer as it provides ancillary services in addition to stepping-up/down of grid voltages. Each active switch in SST is a potential source of failure, and a quick fault detection and isolation help in reducing the downtime. In this article, a novel fault detection and localization algorithm is proposed to localize the open-circuit (OC) faults (be it a single switch fault or multiple switch faults in multiple modules) in the cascaded H-bridge (CHB) stage of the SST. The proposed method is designed such that only one voltage sensor is needed to measure the grid-side voltage of the CHB for fault detection and localization. By comparing the estimated grid-side voltage of the CHB with the measured values, OC fault is detected. Once the fault is detected, the modulation scheme is switched from phase-shifted unipolar sinusoidal pulsewidth modulation (SPWM) to phase-shifted bipolar SPWM to localize the faults. The difference in the measured grid-side voltage of the CHB before and after each module's state change is calculated and faults are localized. The presented algorithm is substantiated using experimental results from a 1-kVA SST prototype. The main advantage of the proposed algorithm is that it requires only 2n comparisons to localize the faulty modules in a CHB with n H-bridges, which takes less than a line cycle to complete.

关键词： Circuit faults Switches Voltage measurement fault detection Switching frequency Phase modulation Cascaded multilevel converter (MMC) fault detection and localization multiple switch failures open-circuit (OC) faults solid-state transformer (SST)

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The variant cycle-cover problem in fault detection and localization for mesh all-optical networks

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PHOTONIC NETWORK COMMUNICATIONS 2007年第2期14卷 111-122页

作者： Zeng, Hongqing Vukovic, Alex Cisco Syst Inc Ottawa ON K2K 3E8 Canada Commun Res Ctr Ottawa ON K2H 8S2 Canada

With the soaring channel speed and density in all-optical networks (AONs), the risk of high data loss upon network faults increases quickly. To manage network faults efficiently, an m-cycle based fault detection and localization (MFDL) scheme has been introduced recently. This paper verifies the necessary and sufficient condition for achieving the complete fault localization (CFL) in MFDL, which is defined as the case that every single network fault can be located to a unique link. We model the m-cycle construction as a new mathematical problem: the variant version of the constrained cycle-cover problem (vCCCP) and explore its formal expression. The model includes the consideration of the cycle-length limit, cycle number, and wavelength cost, while also keeps the CFL achievable. A two-phase branch-and-bound (B&B) algorithm was developed for solving the vCCCP, which guarantees to find near-optimal solutions. This algorithm is then applied to four typical and four random network examples to validate and assess the performance. The results are analyzed and compared with some previously reported algorithms, in terms of fault localization degree, cycle number, wavelength overhead, and cost reduction. The performance evaluation and comparison reveal that the new model and algorithm could significantly reduce the MFDL cost, including both the cost of monitoring devices and reserved wavelengths.

关键词： fault detection and localization cycle cover all-optical network monitoring cycle

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An Open-Circuit fault detection and localization Scheme for Cascaded H-Bridge Multilevel Converter Without Additional Sensors

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 457-467页

作者： Gorla, Naga Brahmendra Yadav Kolluri, Sandeep Chai, Merlin Panda, Sanjib Kumar Natl Univ Singapore Dept Elect & Comp Engn Singapore 117580 Singapore

In this article, a new model-based fault detection and localization scheme is proposed for cascaded H-bridge (CHB) multilevel converter to localize the open-circuit faults without needing any additional sensors. The grid current is estimated from the gird voltage, switching states, and dc bus voltages, and compared with the measured grid current. Any significant deviations in the estimated and actual grid currents of the CHB trigger an open-circuit (OC) fault. Once the fault is detected, modulation scheme is switched from phase-shifted unipolar SPWM to phase-shifted bipolar sine pulse width modulation (SPWM) to localize the OC faults. At selected time instants, the change in measured grid current for a specific period is calculated and compared with the estimated change in grid current from the system model. The abovementioned comparisons are used to localize the OC faults in the CHB. The proposed method is validated on a scale-down CHB prototype developed in the laboratory environment, and experimental results are presented to demonstrate the effectiveness of the proposed method.

关键词： Circuit faults Switches Multilevel converters Voltage measurement Voltage Location awareness fault detection Cascaded H-bridge fault detection and localization multilevel converters sensorless fault detection

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Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

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WEB INTELLIGENCE 2024年第2期22卷 151-166页

作者： Freeda, Adline R. Rajendran, Selvi P. Hindustan Inst Technol Sci Dept Comp Sci & Engn Kelambakkam 603103 Tamil Nadu India

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized BlackWidow Optimization-based fault detection and localization approach can provide superior outcomes.

关键词： Debugging Software testing Test suite minimization Blue Monkey Customized Black Widow Algorithm fault validation Improved Long Short-Term Memory (LSTM) fault detection and localization

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Deep Learning for Hardware-Based Real-Time fault detection and localization of All Electric Ship MVDC Power System

IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS

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IEEE OPEN JOURNAL OF INDUSTRY APPLICATIONS 2020年 1卷 194-204页

作者： Liu, Qin Liang, Tian Dinavahi, Venkata Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

The tendency toward electrification of marine vessels has led the evolution of the all electric ship (AES). The harsh operating environment of the AES makes the shipboard power system (SPS) vulnerable, so a powerful monitoring system for fault detection and localization (FDL) is essential for safe navigation. We propose a machine learning based FDL method for monitoring the system condition with the problem of imbalanced training dataset. The generative adversarial network (GAN) comprising of deep convolutional neural networks was employed to synthesize numerous valid samples. Feature extraction and selection technologies were applied to time-series signals to reduce features for monitor training. Finally, the random forest (RF) model was trained using the augmented training dataset, combining real data with generated ones by GAN, to verify the capability of the GAN-RF based FDL method. Both real training and testing data were collected from the SPS model established in PSCAD/EMTDC. The results demonstrated that the monitor could distinguish different conditions in real-time with the help of hardware implementation on the FPGA and a 99% classification accuracy was achieved with excellent anti-noise capability.

关键词： All electric ship correlation based feature selection deep convolutional neural networks fault detection and localization field-programmable gate array generative adversarial networks multivariate empirical mode decomposition mediumvoltage direct current machine learning random forest real-time systems

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Design of a Scheme fault detection and localization in Piecewise Linear Systems 17

Design of a Scheme Fault Detection and Localization in Piece...

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17th IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： Morales-Morales, C. Morales-Rodriguez, V. A. Adam-Medina, M. Vela-Valdes, L. G. Univ Politecn Estado Morelos UPEMOR Ingn Elect & Telecomunicac Jiutepec Morelos Mexico CENIDET Ctr Nacl Invest & Desarrollo Tecnol Dept Ingn Elect Cuernavaca Morelos Mexico

ISBN: (纸本)9781467371216

This paper propose at design of the scheme of fault detection and localization for piecewise linear systems in discrete time. The operation modes that integrate the piecewise linear systems are switched for one switching law, dependent of time and out of the system. For each active operation mode in a given time interval is considered unknown, because we don't know in this time instant one mode must be activated. We proposed that observer's bank distinguish the active operation mode and thus achieved to estimate of states of the dynamic system. So that, the scheme fault detection and localization is developed using a piecewise linear observer for residual generation and fixed thresholds for residual evaluation, then the fault could be detected and located. The faults cases consider in this paper are additive faults, known as sensor faults and their validation uses a numerical example.

关键词： Piecewise Linear System Piecewise linear Observer fault detection and localization

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p-Cycles as Their Own m-Cycles for fault detection and localization in Elastic Optical Networks

p-Cycles as Their Own m-Cycles for Fault Detection and Local...

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International Conference on VLSI, Communications, and Signal Processing (VCAS)

作者： Jaiswal, Deo Chandra Asthana, Rachna Dept Elect Engn HBTU Kanpur Uttar Pradesh India

ISBN: (纸本)9789813297753;9789813297746

In the newgeneration, the requirement of transmission of information is an unavoidable need for an exponentially growing traffic demand which is provided by elastic optical networks. Asmall disruption in these networks interrupts an enormous amount of transferring of data among common as well as prime users. Network survivability has, therefore, become a necessity for uninterrupted network services. In order to achieve the network survivability, preconfigured protection cycle (p-cycle) structures due to their fast restoration speed as well as the capacity efficiency are utilized with additional resources to cater the backup protection. p-Cycles protect span, with the presumption of having the knowledge of failed span in real time. In case of failed span is not known the fault detection and localization is required before providing protectionwhich is done by monitoring cycles (m-cycles). It is investigated that p-cycle structures which are used for protection can also be made able to function as m-cycles for fault detection and localization. An Integer Linear Programming (ILP) model is developed to minimize total spare capacity considering p-cycles also performing the function of m-cycles. Numerical results reveal that there is no or fewer extra cost required in case of combined p-cycles with m-cycles compared to pure p-cycles. The reason for no extra cost in combined p-cycles withm-cycles is that here some p-cycles are performing the dual role of protection as well as detection and localization of failed span of the network.

关键词： Elastic optical network fault detection and localization m-Cycles p-Cycles Survivability

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