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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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fault detection and classification in automated assembly machines using machine vision

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第9-12期90卷 2491-2512页

作者： Chauhan, Vedang Surgenor, Brian Queens Univ Mech & Mat Engn Dept Kingston ON K7L 3N6 Canada

Automated assembly machines operate continuously to achieve high production rates. Continuous operation increases the potential for faults such as jams, missing parts, and electromechanical failures of subsystems. The goal of this research project was to develop and validate a machine vision inspection (MVI) system to detect and classify multiple faults using a single camera as a sensor. An industrial automated O-ring assembly machine that places O-rings on to continuously moving plastic carriers at a rate of over 100 assemblies per minute was modified to serve as the test apparatus. An industrial camera with LED panel lights for illumination was used to acquire videos of the machine's operation. A programmable logic controller (PLC) with a human-machine interface (HMI) allowed for the generation of faults in a controlled fashion. Three MVI methods, based on computer vision techniques available in the literature, were developed for this application. The methods used features extracted from the videos to classify the machine's condition. The first method was based on Gaussian mixture models (GMMs);the second method used an optical flow approach;and the third method was based on running average and morphological image processing operations. In order to provide a single metric to quantify relative performance, a machine vision performance index (MVPI) was developed with five measures of performance: accuracy, processing time, speed of response, robustness against noise, and ease of tuning. The MVPI for the three MVI methods is reported along with the significance of the results.

关键词： Machine vision Automated assembly machine fault detection and classification Performance index

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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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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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fault detection and classification in Plasma Etch Equipment for Semiconductor Manufacturing e-Diagnostics

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2012年第1期25卷 83-93页

作者： Hong, Sang Jeen Lim, Woo Yup Cheong, Taesu May, Gary S. Myongji Univ Dept Elect Engn Yongin 449728 South Korea Georgia Inst Technol Dept Ind & Syst Engn Atlanta GA 30332 USA Georgia Inst Technol Dept Elect Commun Engn Sch Engn Atlanta GA 30332 USA

A method of fault detection and classification (FDC) for semiconductor manufacturing equipment e-diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing;however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster-Shafer (D-S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency (RF) power source module, RF power bias module, gas delivery module, and process chamber module, are presented by combining related parameters, and successful fault detection is achieved. The evidential reasoning of RF probe is also beneficial for the detection of chamber leak simulation, and the classification of fault is made by further investigating voltage signal of RF probe. Successful fault detection in subsystem level with zero missed alarms was demonstrated using D-S theory of evidential reasoning, and the classification for finding root cause of the fault is presented in the chamber leak fault simulation. We realized that successful FDC can be accomplished by combining various related information and by incorporating engineering expert knowledge.

关键词： Dempster-Shafer theory fault detection and classification modular neural networks semiconductor equipment diagnosis

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fault detection and classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2014年第11期63卷 2651-2665页

作者： Li, Weilin Monti, Antonello Ponci, Ferdinanda Northwestern Polytech Univ Dept Elect Engn Xian 710072 Peoples R China Rhein Westfal TH Aachen EON Energy Res Ctr D-52074 Aachen Germany

This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.

关键词： Artificial neural networks (ANNs) fault detection and classification medium voltage dc (MVDC) system wavelet transform (WT)-based multiresolution analysis (MRA)

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fault detection and classification in chemical processes using NMFSC and structural SVMs

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2014年第6期92卷 1016-1023页

作者： Peng, Yi Chen, Xiaogang Ye, Qixiang Jiao, Jianbin Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

We propose a novel fault detection and classification approach via Non-negative Matrix Factorisation with Sparseness Constraints (NMFSC) and Structural Support Vector Machines (Structural SVMs). The NMFSC method can not only reduce data dimension, but also better describe the local feature of the process because of non-negative and sparseness constraints. The function of Structural SVMs is to identify multiple kinds of faults using only one uniform discriminative model instead of multiple ones. Tennessee Eastman Process (TEP), a benchmark chemical engineering problem, is used to generate datasets to evaluate the performance of the proposed approach. The results from the experiment show the superiority of the new method compared with the state-of-the-art approaches.

关键词： structural support vector machines non-negative matrix factorisation with sparseness constraints fault detection and classification chemical processes

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fault detection and classification with feature representation based on deep residual convolutional neural network

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JOURNAL OF CHEMOMETRICS 2019年第9期33卷 e3170-e3170页

作者： Ren, Xuemei Zou, Yiping Zhang, Zheng Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

This paper proposes a novel fault detection and classification method via deep residual convolutional neural network (DRCNN). The DRCNN captures the deep process features represented by convolutional layers from local to global. Unlike traditional methods, this feature representation can extract the deep fault information and learn the latent fault patterns. Besides, a data preprocessing approach is also proposed to transform the shape of original data into the shape available for convolutional neural network. Finally, experiments based on the data set of Tennessee Eastman process (TEP), a chemical industrial process benchmark, show that the proposed method achieves superior fault detection and better classification performance compared with the state-of-the-art methods.

关键词： chemical processes convolutional neural network fault detection and classification feature representation

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fault detection and classification using artificial neural networks

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IFAC-PapersOnLine 2018年第18期51卷 470-475页

作者： Heo, Seongmin Lee, Jay H. Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of

Process monitoring is considered to be one of the most important problems in process systems engineering, which can be benefited significantly from deep learning techniques. In this paper, deep neural networks are applied to the problem of fault detection and classification to illustrate their capability. First, the fault detection and classification problems are formulated as neural network based classification problems. Then, neural networks are trained to perform fault detection, and the effects of two hyperparameters (number of hidden layers and number of neurons in the last hidden layer) and data augmentation on the performance of neural networks are examined. fault classification problem is also tackled using neural networks with data augmentation. Finally, the results obtained from deep neural networks are compared with other data-driven methods to illustrate the advantages of deep neural networks. © 2018

关键词： fault detection Deep learning Deep neural networks Neural networks Process monitoring Data augmentation Data driven methods fault classification fault detection and classification Hidden layers Hyperparameters In process Learning techniques

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fault detection and classification for Wind Turbine Gearbox via a Time-Frequency Analysis Method 9

Fault Detection and Classification for Wind Turbine Gearbox ...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Gu, Caoyuan Zhu, Junwei Huang, Dajian Wang, Xin Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Heilongjiang Univ Coll Math Sci Harbin 150080 Peoples R China

ISBN: (纸本)9781728159225

Gearbox with complex structure is one of the most fragile components of wind turbines. The fault detection and classification of the gearbox is crucial to reduce the unexpected downtime and economic loss of wind turbines. The main purpose of this paper is to propose a fault detection and classification method based on standard deviation and wavelet entropy for wind turbines. Firstly, the fault detection logic is designed by using the standard deviation and its normalized result. Secondly, the wavelet entropy is used to determine the wind turbine gearbox fault type. The experimental results from historical data are consistent with the actual maintenance report results, which verifies the effectiveness of the method.

关键词： Wind turbine gearbox fault detection and classification Standard deviation Wavelet entropy

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