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Phaselet-Based Limited-Time $q$-Axis DC Offset Voltage Injection for detection of Islanding of DG and fault in a Meshed Microgrid

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 1379-1390页

作者： Kumar, Avinash Mohapatra, Abheejeet Singh, Sri Niwas Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

Rapid grid integration of inverter-interfaced distributed generators (DGs) and low-voltage ride-through capability of the smart inverter, as per IEEE 1547 standard, has made unintentional islanding detection (UID) cumbersome. Also, the feeder reconfiguration capability in meshed microgrids with multiple DGs has made the existing UID techniques ineffective. The methods have limitations in UID for small power mismatch conditions characterizing nondetection zone (NDZ). In hybrid methods for UID, a disturbance injection is continuous or for an extended period, which degrades the power quality (PQ) and creates stability issues. Hence, a decentralized closed-loop hybrid islanding detection method (HIDM) based on the indices extracted from the subcycle phaselet (passive) technique with limited-time (only for two cycles) $q$-axis dc offset voltage injection (active) is proposed for UID of DGs. The proposed HIDM mitigates the NDZ due to the closed-loop scheme. Subcycle estimations from the phaselet technique result in quick UID and fault detection and classification process. Furthermore, the PQ issue is reduced due to the limited-time voltage injection (based only on the command from phaselet technique) in the proposed HIDM. Simulation results from the real-time digital simulator on a meshed microgrid and performance comparison with other existing methods prove the efficacy of the proposed HIDM.

关键词： Transient analysis Estimation Microgrids Voltage control Reactive power Indexes Harmonic analysis Distributed generators (DGs) fault detection and classification islanding detection microgrid phaselet

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An Autoencoder-Based Approach for fault detection in Multi-Stage Manufacturing: A Sputter Deposition and Rapid Thermal Processing Case Study

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 166-173页

作者： Jebril, Hana T. T. Pleschberger, Martin Susto, Gian Antonio Med Univ Vienna Christian Doppler Lab Artificial Intelligence Ret A-1090 Vienna Austria Kompetenzzentrum Automobil & Ind Elekt GmbH DSC Dept A-9524 Villach Austria Univ Padua Dept Informat Engn I-35131 Padua Italy

Data-driven fault detection and classification approaches are becoming increasingly important in semiconductor manufacturing and in other industries aiming at implementing the Zero-defect paradigm. Two of the main challenges in developing such solutions are: (i) the complexity of sensor data, that typically presents themselves in the form of time-series, requiring the employment of time-consuming and possibly sub-optimal feature extraction approaches;(ii) the fact that faults/defects may be caused by more than a single process, but in many cases they are generated by a cascade of processes. In this paper, we tackle the first issue, by considering a two-stage case study consisting of a deposition process and a rapid thermal process. The proposed approach is based on convolutional deep autoencoders employed to perform feature extraction from time-series sensor data in frontend production equipment. We will show on the reported case study, how the proposed approach outperfoms key numbers-based approaches typically used in the industry. To allow reproducibility of the reported results and to foster research in the field, we publicly share the data used in this work.

关键词： Feature extraction Convolution Production Semiconductor device manufacture Task analysis fault detection Decoding Convolutional autoencoder deep learning fault detection and classification machine learning multi-stage fabrication semiconductor manufacturing

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fault detection and classification in a Distribution Network Integrated with Distributed Generators

Fault Detection and Classification in a Distribution Network...

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IEEE-Power-Engineering-Society Conference and Exposition in Africa (PowerAfrica)

作者： Adewole, A. C. Tzoneva, R. Cape Peninsula Univ Technol Ctr Substn Automat & Energy Management Syst Cape Town South Africa

ISBN: (纸本)9781467325509;9781467325486

This paper develops a methodology for application in distribution network fault detection and classification. The proposed methodology is based on wavelet energy spectrum entropy decomposition of disturbance waveforms to extract characteristic features by using level-4 db4 wavelet coefficients. Thus, few input features are required for the implementation. Different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, and load switching are applied to the IEEE 34 Node Test Feeder. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. Extensive studies, verification, and analysis made from the application of this technique validate the approach. Comparison with statistical methods based on standard deviation and mean absolute deviation has shown that the method based on log energy entropy is very reliable, accurate, and robust.

关键词： Discrete wavelet transform distribution network fault detection and classification wavelet energy spectrum

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On the Investigation of Phase fault classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

On the Investigation of Phase Fault Classification in Power ...

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North American Power Symposium (NAPS)

作者： Galbraith, Kelli Alaca, Ozgur Ekti, Ali Riza Wilson, Aaron Snyder, Isabelle Stenvig, Nils M. Oak Ridge Natl Lab Elect & Energy Infrastruct Div Oak Ridge TN 37830 USA Slippery Rock Univ Penn Dept Math & Stat Slippery Rock PA USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9798350315097

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers;the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

关键词： fault detection and classification feature extraction machine learning phase fault smart grid systems

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detection and classification of Transmission Line Transient faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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Explainable artificial intelligence-based evidential inferencing on process faults in plasma etching

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2024年第18期57卷 185201-185201页

作者： Choi, Jeong Eun An, Surin Lee, Younji Lee, Yongil Kim, Dohyun Hong, Sang Jeen Myongji Univ Dept Elect Engn Yongin 17058 South Korea Myongji Univ Dept Ind & Management Engn Yongin 17058 South Korea Myongji Univ Dept Semicond Engn Yongin 17058 South Korea

The fault detection and classification (FDC) modeling proposed in this study is a research approach that is intended to improve the performance of plasma process models by leveraging optical emission spectroscopy (OES) data containing plasma information (PI) and enhancing model interpretability using explainable artificial intelligence (XAI) algorithms. Status variable identification data that included normal and abnormal states of bias power, pressure, SF6 gas flow, and O2 gas flow were collected during a silicon etching process with SF6, O2 gas plasma. Additional variables were derived from the OES data and included additional PI, such as O and F radicals, which were computed using actinometry, and electron temperature and electron density computed using the line ratio method. By building a high-performance FDC model and interpreting its results using XAI algorithms, we propose solutions to the limitations of the FDC model in semiconductor plasma processes.

关键词： fault detection and classification machine learning optical emission spectroscopy explainable artificial intelligence plasma etching

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Real-Time detection and classification of Bypass Diode-Related faults in Photovoltaic Modules via Thermoelectric Devices

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ADVANCED MATERIALS TECHNOLOGIES 2024年第2期9卷

作者： Ko, Jaehwan Kim, Chungil Lee, Deukgwang Lee, Suwoon Shin, Woo Gyun Kang, Gi Hwan Oh, Jaewon Ko, Suk Whan Song, Hyung-Jun Seoul Natl Univ Sci & Technol Dept Safety Engn 232 Gongneung Ro Seoul 01811 South Korea Korea Inst Energy Res Photovolta Lab 152 Gajeong Ro Daejeon 34129 South Korea Univ North Carolina Charlotte Dept Engn Technol & Construct Management 9201 Univ City Blvd Charlotte NC 28223 USA

The accurate detection and classification of faults in photovoltaic (PV) systems contribute to enhancing their performance. Bypass diode (BD) heating is a common issue afflicting field-installed PV modules. Although different fault modes exist, they exhibit similar characteristics, which hinder their early detection and classification. Thus, a real-time fault detection and classification method for heated BDs in PV modules using thermoelectric devices (TEs) is proposed. When PV modules experience partial shading (PS) or exhibit bypass diode faults (BDFs), the temperature of the BD rises. By attaching a TE to the BD, the heat generated due to the PS or BDF can be converted into electrical energy. Additionally, the cause of bypass heating can be classified in real time by analyzing the TE's power-generation capacity, considering the inverter status and ambient conditions. For example, the power generated by the TE is in the ranges of 8.5-14.7 (PS) and 28-49.4 mW cm-2 (BDF) under operating conditions. Moreover, the power generated by the TE under the PS and BDF conditions is sufficiently high to drive a communication system, guaranteeing the reliability of the detection system. Therefore, the proposed bypass heating detection system can classify the fault modes of PV systems in real time without external power. The proposed method discussed in this paper aims to detect and classify fault modes of bypass diode in photovoltaic modules through the use of self-powered thermoelectric devices. By analyzing the heating patterns of the bypass diode, the method can effectively distinguish between bypass diode faults and partial shading without the need for any external power ***

关键词： bypass diode fault detection and classification maintenance and operation photovoltaic module thermoelectric

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Intelligent protective relaying for the series compensated line with high penetration of wind energy sources

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 163卷

作者： Mohanty, Subodh Kumar Nayak, Paresh Kumar Siano, Pierluigi Swetapadma, Aleena KIIT Deemed Be Univ Sch Engn Bhubaneswar India Indian Inst Technol ISM Dept Elect Engn Dhanbad India Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

The bulk amount of power generated from the present-day large-scale DFIG-installed wind farms are preferably transmitted to utility grid through series compensated transmission lines. Currently, TCSC compensation is more attractive compared to fixed series compensation due to its numerous technical advantages. However, the nonlinear relationship of the output power verses wind speed and the different operating modes of the DFIG and TCSC cause rapid variation in the line impedance during both normal as well as fault conditions. Consequently, the widely used fixed impedance-based distance relays when used for protection of such lines find limitation. In this paper, a fast discrete S-transform feature-assisted back propagation neural network technique is proposed using the relay end current measurements for effective detection and classification of faults in such crucial transmission lines. The efficacy of the scheme is evaluated on numerous fault and non-fault cases simulated through MATLAB/Simulink on different standard test systems under varying system operating conditions. The results clearly show the superiority of the proposed method in comparision to the existing approaches in terms of its low computational burden, fast fault detection time (< 10 ms) and accuracies (= 100 %) and fast fault classification time (< 10 ms) and accuracies (99.99 %).

关键词： Back-propagation neural network DFIG-installed wind farm fault detection and classification S -transform TCSC-compensated lines

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Evaluation of fault detection Algorithms for Photovoltaic Array Using Distributed Machine Learning Platform 22

Evaluation of Fault Detection Algorithms for Photovoltaic Ar...

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22nd National Power Systems Conference (NPSC)

作者： Choumal, Apoorva Yadav, Vinod Kumar Delhi Technol Univ Elect Engn Dept Delhi India

ISBN: (纸本)9781665462020

The protection of solar photovoltaic (PV) systems has become immensely important in the last several decades due to the exponential growth in the World's PV power capacity. As a result, fault analysis in PV arrays has evolved into a crucial task for protecting PV modules from damage and minimizing the possibility of occupational hazards. However, PV systems are covered by standard protection mechanisms, although some faults with low mismatch and high fault impedance levels may go undetectable. It might be challenging to discern an anomaly from normal operation due to the often-minute changes in electrical signal magnitude caused by malfunctioning photovoltaic components during such faults. In such cases, datadriven machine learning methods give reliable detection and classification results. This paper presents a workflow to use a machine-learning library of a distributed computing framework, PySpark. PySpark, a Python API for Apache Spark, is a powerful computational engine that efficiently handles enormous data volumes. The key characteristics of I-V curves under various fault occurrences and standard conditions are extracted from a MATLAB simulation of the PV Module for fault detection on the dc side of the PV array. The ML library in PySpark is then used to examine these attributes and detect faults. A confusion matrix addressing soft accuracy, precision, recall, etc., is used in a comparative analysis of several classification methods.

关键词： Photovoltaic (PV) faults data-driven PySpark apache spark machine learning fault detection and classification PV monitoring

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NONLINEAR MAPPING LYAPUNOV EXPONENTS-BASED KNN ANALYSIS FOR fault classification

NONLINEAR MAPPING LYAPUNOV EXPONENTS-BASED KNN ANALYSIS FOR ...

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Atique, Md Saifuddin Ahmed Yang, Cai Xia Univ North Dakota Grand Forks ND 58202 USA

ISBN: (纸本)9780791886670

Rotating machinery are widely used in industry. Machine failures may result in costly downtime. Without effective fault diagnosis, it is impossible to predict the lead-time to failure. Therefore, conducting effective fault detection and identification are desirable and imperative. However, diagnosing faults in rotating machinery is often a labor-intensive and time-consuming practice. This makes conducting effective and efficient fault diagnosis a challenge for technicians and plant maintainers. This paper presents a Lyapunov Exponents-Based K-Nearest Neighbor (LE-Based KNN) analysis method for detecting and classifying rotating machinery fault. To distinguish different machine conditions, Lyapunov exponents (LEs) calculated using nonlinear mapping were selected as features in KNN analysis for classifying the signature of different faults from the machine vibration. The LEs from forty-four vibration recordings served as training dataset for KNN analysis, and the machine health conditions were grouped into four categories. Data from unknown machinery health condition were used as testing data. The results show that proposed LE-Based KNN analysis method can achieve reliable fault classification on operating condition. The LE-Based KNN analysis method has potential improvement in classification with modified KNN and optimized feature selection. This approach can be used to acquire a complete machine vibration profile that may predict occurrence of damage in different parts of machine and help to detect and identify the defective machineries at early stages.

关键词： Lyapunov exponents nonlinear mapping fault detection and classification k-nearest neighbor analysis rotating machinery

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