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fault detection and classification for Wide Area Backup Protection of Power Transmission Lines Using Weighted Extreme Learning Machine

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IEEE ACCESS 2022年 10卷 82407-82417页

作者： Harish, Ani Prince, A. Jayan, M., V APJ Abdul Kalam Technol Univ Rajiv Gandhi Inst Technol Kottayam Dept Elect Engn Thiruvananthapuram 695016 Kerala India APJ Abdul Kalam Techol Univ Thiruvananthapuram 695016 Kerala India

The changing landscape of power grids with distributed energy sources and power electronic devices has led to increasing relay maloperations. Wide area backup protection is necessary for the resolution of faults and for a reliable power grid. This paper presents detecting and classifying faults in transmission lines for wide-area backup protection using phasor measurement units (PMU) data. The faults are detected and classified using a Weighted Extreme Learning Machine (WELM) algorithm, which considers the variable distribution of data among the different classes using a weighted approach. The PMU signal data used were generated by the simulation of an IEEE 39 bus test system in the PowerWorld/OpenPDC/MATLAB environment. For classification, the input features data were derived using a wavelet transform-based ensemble feature extraction technique, and the WELM classifier was optimized using Particle Swarm Optimization (PSO). The PSO optimized WELM (PSO-WELM) model trained on PMU data detected faults with 100% accuracy and classified them into different types with an accuracy of 99.85%. It is validated that the PSO-WELM outperforms other known classifiers on performance comparison. The model also classified noisy data with a signal-to-noise ratio (SNR) as low as 10 dB and with an accuracy of 97%.

关键词： Data fault transmission lines fault detection and classification phasor measurement unit extreme learning machine machine learning

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Intelligent fault detection and classification for an Unbalanced Network With Inverter-Based DG Units

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第5期20卷 7325-7334页

作者： Pandey, Avinash Kumar Kishor, Nand Mohanty, Soumya R. Samuel, Paulson Motilal Nehru Natl Inst Technol Dept Elect Engn Prayagraj 211004 India Ostfold Univ Coll Dept Engn N-1671 Fredrikstad Norway Indian Inst Technol BHU Dept Elect Engn Varanasi 221005 India

In this article, a machine-learning-based fault detection and classification method is proposed. Two supervised learning-based protection modules are developed for the relays considered in the study-one to detect the fault and discriminate between the symmetrical or unsymmetrical nature of the fault and another to detect the faulty phase(s). A robust set of features using both relay voltage and current signals is utilized for developing the modules. The features are obtained using the multiresolution decomposition based on the empirical wavelet transform. The modules are tested on the unbalanced IEEE 13-node network integrated with inverter-based distributed generation systems capable of reactive power injection in the low-voltage ride-through mode of operation. Varying penetration levels and intermittent output of distributed generation, fault resistance, fault inception time, noise in the signals, and switching events occurring around the fault period are some of the unique operating scenarios considered to demonstrate the consistent performance of the relays. The simulations are performed in power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) and the protection modules are developed in MATLAB. The performance of the protection modules is evaluated using several metrics with respect to various classes of events occurring in the network.

关键词： Empirical wavelet transform (EWT) fault detection and classification low-voltage ride through (LVRT) machine learning (ML) protection modules

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An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

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ELECTRICAL ENGINEERING 2024年第5期106卷 6199-6212页

作者： Deb, Anindita Jain, Arvind Kumar NIT Agartala Dept Elect Engn Jirania 799046 Tripura India NIT Agartala Fac Engn Dept Elect Engn Jirania 799046 Tripura India

DC microgrids are gaining more importance in maritime, aerospace, telecom, and isolated power plants for heightened reliability, efficiency, and control. Yet, designing a protective system for DC microgrids is challenging due to novelty and limited literature. Recent interest emphasizes standalone fault detection and classification, especially through data-driven machine-learning approaches. However, the emphasis remains on progressing state-of-the-art tools for fault diagnosis in DC microgrids. Therefore, this work emphasizes fault detection and classification in a low-voltage standalone DC microgrid using a data-driven machine learning hybrid approach: bagged ensemble learner and cosine k-nearest neighbour (C-kNN) algorithms. The proposed fault detection and classification scheme makes the use of local voltage and current measurements which enhances the admissibility of the proposed scheme. The bagged ensemble learner accurately identifies the faults in the line, whereas the cosine k-nearest neighbor classifies the fault as pole to ground or pole to pole for further corrective actions. A diverse set of test scenarios encompassing faulty and normal conditions has been analyzed and validated by randomizing data inputs. The test model comprising PV, battery source, and loads have been constructed in MATLAB/Simulink environment. The proposed scheme promises accurate fault identification and classification in normal and noisy environments. To establish the robustness of the proposed approach, the outcomes of the fault detection and classification scheme have been compared with the methods reported in the literature. The results indicate that the proposed method outperformed in comparison to existing methods.

关键词： fault detection and classification Bagged Ensemble learner Cosine k-nearest neighbor Low voltage DC MG Confusion matrix

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fault detection and classification for Photovoltaic Systems Based on Hierarchical classification and Machine Learning Technique

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第12期68卷 12750-12759页

作者： Eskandari, Aref Milimonfared, Jafar Aghaei, Mohammadreza Amirkabir Univ Technol Dept Elect Engn Tehran 158754413 Iran Eindhoven Univ Technol Dept Mech Engn Energy Technol NL-5600 Eindhoven Netherlands Albert Ludwigs Univ Freiburg Fac Engn IMTEK Lab Elect Instrumentat Georges Koehler Allee 10 D-79110 Freiburg Germany

Line-line (LL) and line-ground (LG) faults may not be detected by common protection devices in Photovoltaic (PV) arrays as these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. In this article, we propose a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the dc side of PV systems. For this purpose, the main features of current-voltage curves under different fault events and normal conditions are extracted. The faults are categorized using the hierarchical classification platform. Later, the LL and LG faults are detected and classified by machine learning methods. The proposed method aims to reduce the amount of dataset, which is required for the learning process, and also obtains a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared with other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under different conditions and severity with the accuracy of 96.66% and 91.66%, respectively.

关键词： Circuit faults Feature extraction Impedance fault detection Data models classification algorithms Monitoring fault detection and classification hierarchical classification (HC) line-line (LL) and line-ground faults (LG) machine learning (ML) photovoltaic monitoring

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Single fuzzy inference based fault detection and classification protection scheme for different types of short circuit faults on double circuit transmission lines

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2025年第1期45卷 218-236页

作者： Vikram Raju, Gotte Srikanth, Nandiraju Venkata Natl Inst Technol Elect Engn Dept Warangal 506004 Telangana India

An intelligent protection scheme based on the single fuzzy inference system and discrete Fourier transform is proposed for the faulty phase detection and classification of faults on the mutually coupled double circuit transmission lines. This proposed protection technique uses the magnitude of the pre-processed current information of the sending end bus only. This is implemented in the MATLAB/Simulink environment on a 400 kV, 50 Hz, and 300 km double circuit power system model. The efficacy of the proposed scheme has been tested by performing a wide range of simulation studies concerning different types of faults (common short circuit faults, cross-country and evolving faults, and high impedance faults) and typical fault scenarios (current transformer saturation, noisy environment, and fault during power swing conditions) with variations in different fault parameters and operating conditions. The results presented confirm that the proposed method detects/classifies all the faults within one cycle time and is reliable with a detection/classification accuracy of 99.75%. It is immune to variations in fault parameters, and different operating conditions, and is not affected by the zero sequence mutual impedance. It does not require any training and communication link. Further, the performance is also appraised with other training-based protection schemes.

关键词： Double circuit transmission lines discrete fourier transform protection scheme fuzzy inference system fault detection and classification

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Cross-Chamber Data Transferability Evaluation for fault detection and classification in Semiconductor Manufacturing

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2023年第1期36卷 68-77页

作者： Zhu, Feng Jia, Xiaodong Li, Wenzhe Xie, Min Li, Lishuai Lee, Jay City Univ Hong Kong Dept Adv Design & Syst Engn Hong Kong Peoples R China Univ Cincinnati Ctr Intelligent Maintenance Syst Cincinnati OH 45221 USA City Univ Hong Kong Dept Adv Design & Syst Engn Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Unit-to-unit variation among the production chambers is a long-lasting challenge for fault detection and classification (FDC) development in the semiconductor industry. Currently, various methods are applied for knowledge transfer among chambers and generalized FDC model development. However, the existing methods cannot give a quantitative or qualitative measure for cross-chamber data transferability evaluation. This research proposes a novel methodology for data transferability evaluation and important sensor screening, which can serve as a data quality evaluation tool for any FDC model. In this research, firstly, Time Series Alignment Kernel (TSAK) is incorporated into Multidomain Discriminant Analysis (MDA) algorithm to achieve sensor-based domain generalization. Then, domain-invariant features are directly extracted for sensor visualization. After that, Fisher's criterion ratios of the labeled good wafer samples and defective ones are computed based on the domain-invariant features of each sensor to quantitatively estimate how easy it is to transfer knowledge of each sensor among chambers, i.e., data transferability evaluation. Lastly, the proposed method develops a Recursive Feature Elimination (RFE)-based sensor selection algorithm to qualitatively analyze the importance of each sensor channel and identify the critical sensor subset. In this study, validation of the proposed method is based on two open-source datasets from real production lines.

关键词： Feature extraction Data models Semiconductor device modeling Kernel fault detection Time series analysis Production Semiconductor timer series alignment kernel domain generalization sensor selection fault detection and classification

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Hybrid Deep Learning Model for fault detection and classification of Grid-Connected Photovoltaic System

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IEEE ACCESS 2022年 10卷 13852-13869页

作者： Alrifaey, Moath Lim, Wei Hong Ang, Chun Kit Natarajan, Elango Solihin, Mahmud Iwan Juhari, Mohd Rizon Mohamed Tiang, Sew Sun UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia

Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present

关键词： Feature extraction fault detection Wavelet packets Support vector machines Maintenance engineering Renewable energy sources Photovoltaic systems Deep distributed energy equilibrium optimizer algorithm (EOA) fault detection and classification grid-connected photovoltaic systems optimal feature selection wavelet packet transform (WPT)

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fault detection and classification for Sensor faults of UAV by Deep Learning and Time-Frequency Analysis 40

Fault Detection and Classification for Sensor Faults of UAV ...

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40th Chinese Control Conference (CCC)

作者： Huang, Jing Li, Mengna Zhang, Youmin Mu, Lingxia Ao, Zihang Gong, Haihua Xian Univ Technol Dept Automat & Informat Engn Xian 710048 Peoples R China Jiujiang Precis Test Technol Res Inst Jiujiang 332000 Jiangxi Peoples R China Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada China Satellite Maritime Tracking & Control Dept Wuxi 320200 Jiangsu Peoples R China

ISBN: (纸本)9789881563804

Sensor faults could occur in unmanned aerial vehicles (UAVs) during a mission of flight, which might deteriorate UAV's performance or even cause catastrophe. Considering different types of sensor faults of a quadrotor UAV, a new fault detection and classification method based on time-frequency analysis (TFA) and deep learning (DL) technologies is proposed in this paper. Firstly, the data sets including different types of sensor fault in time-domain are generated randomly. The date sets are then transformed into time-frequency domain by short-time Fourier transform (STFT), resulting in time-frequency graph (TFG). Secondly, the time-frequency graph sets are used to train the deep network, by which the fault type can be rapidly classified with high accuracy. Finally, the simulations are carried out to verify the performance of the proposed algorithm and the accuracy reaches to 99.6%.

关键词： fault detection and classification sensor faults classification time-frequency analysis (TFA) deep learning

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Data-Driven fault detection and classification for MTDC Systems by Integrating HCTSA and Softmax Regression

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IEEE TRANSACTIONS ON POWER DELIVERY 2022年第2期37卷 893-904页

作者： Li, Jiapeng Song, Guobing Yan, Jifei Li, Yujun Xu, Zhao Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China

The requirement of fast fault isolation poses a great challenge to the safe operation of multi-terminal direct current (MTDC) systems. In order to make a better tradeoff between the speed and reliability of the protection scheme, it is imperative to mine more valuable information from fault transient signals. This paper puts forward a data-driven framework capable of digging out and synthesizing multi-dimensional features to achieve fast and reliable DC fault detection and classification in MTDC systems. Highly comparative time-series analysis (HCTSA) is first adopted to extract extensive features with clear physical interpretations from fault current waveforms, and a few features valuable to fault identification are then selected utilizing the greedy forward search. Based on the reduced features, a softmax regression classifier (SRC) is further proposed to calculate the probability of each fault category with a relatively minor on-line computational burden. Numerical simulations carried out in PSCAD/EMTDC have demonstrated the proposed approach is effective under different fault conditions, robust against noise corruptions as well as abnormal samplings, and replicable in various DC grids. In addition, comprehensive comparison studies with conventional derivative-based protection methods and some typical artificial intelligence based (AI-based) methods have been conducted. It is verified that the proposed method has the advantages of higher fault identification accuracy over conventional protections and shallow structure AI-based methods, better interpretability as well as lower on-line computing complexity over the deep architecture AI-based approaches.

关键词： Feature extraction Transient analysis fault detection Circuit faults fault diagnosis Time series analysis Artificial intelligence fault detection and classification MTDC system artificial intelligence representation learning

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RFDCS: A reactive fault detection and classification scheme for clustered wsns

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第3期15卷 1705-1732页

作者： Choudhary, Ankur Kumar, Santosh Sharma, Krishna Pal Graph Era Deemed Be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India

Wireless sensor networks (WSNs) are resource-constrained, self-organizing systems that can operate in favorable as well as hostile environments. The deployment capabilities of WSNs, especially in inaccessible and hostile environments, have increased the confidence in the use of WSNs manifold. However, data delivery in sensor networks remains intrinsically faulty and unpredictable. Transmission of erroneous data in the network wastes limited precious resources of the network (such as nodes energy, bandwidth, etc.) and reduces the fidelity of the data. Timely identification of faulty nodes is essential to minimize their consequences, enhance data quality, improve decision making, and extend the lifetime of the network. This work proposes a scheme for detecting and classifying permanent, transient, and intermittent faults in clustered WSNs. The scheme is conversation efficient and reactive. The cluster heads silently analyze information obtained from the nodes during the regular operation for identifying any suspicious behavior. This is done by utilizing moving average and correlation in the obtained data. Cluster heads initiate fault identification process only when they find any suspicious behavior. The presented model localizes certain events which attenuate over distance and neutralize the impact of distance on fault detection accuracy. Thus, our fault diagnosis and classification process utilize correlations that exist in data and handle the problem of measurement variations due to the differences in positions of sensor nodes from the event location when events attenuate over distance. NS-2 based simulation is carried out to evaluate and validate the performance of the proposed scheme. The results show a significant improvement in the performance in terms of fault detection accuracy, false alarm rate, false negative rate, fault classification accuracy and communication overhead.

关键词： Wireless sensor networks fault detection and classification fault tolerance Node faults and failure

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