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International Conference for Convergence of Technology (I2CT)

作者： Kumar, Manoj Mathur, Robin Lovely Profess Univ Sch Comp Engn Phagwara India

ISBN: (纸本)9781479937592

Outlier detection is becoming a recent area of research focus in data mining. In Cloud Computing, we use all resources as a service and these services should be very efficient, robust and corrective. Here we present FDACC (fault detection algorithm for Cloud Computing) for faulty services using outlier detection method that can help to detect accurate and novel faulty services without any knowledge. FDACC implemented on this framework that has three components-cloud nodes, FDS (fault detection System) and End User. FDS is connected with end user and n number of nodes in cloud. FDS is able to detect all non-working services as well as detect faulty services and machines in cloud environment.

关键词： fault detection algorithm Cloud Computing FDACC FDS Outlier detection method

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fault detection and faulty phase(s) identification in TCSC compensated transmission lines

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第6期14卷 1042-1050页

作者： Fasihipour, Hamed Seyedtabaii, Saeed Shahed Univ Elect Engn Dept Tehran Iran

detection of fault in compensated transmission lines during power swing is a complicated task. The distance relay functioning is blocked during stable power swing while it is expected to react as quickly as possible to faults. Non-linear behaviour of metal-oxide varistor and thyristor-controlled series capacitor (TCSC) make even the issue more complicated. In this study, the cumulative sum of the phase current second harmonic is proposed as a unique fault isolation feature. Various low-impedance, high-impedance, symmetric, and asymmetric types of fault signals are collected and to exhibit the algorithm robustness, several disturbance events and measurement noise are applied. The algorithm is conducted over a single machine infinite bus, and a Western System Coordinating Council 9-bus, 3-machine network with TCSC compensated lines. The results of extensive simulations indicate that the algorithm performs excellent regarding some recently developed algorithms. Accurate single, two of three faulty phases are marked despite disturbances and powerful swing. As so, a simple and effective fault detection algorithm has been introduced, which correctly resolves the challenges.

关键词： fault diagnosis varistors power transmission faults thyristor applications power transmission lines power system stability power system harmonics thyristor-controlled series capacitor fault isolation single machine infinite bus Western System Coordinating Council 9-bus 3-machine network TCSC compensated lines fault detection algorithm transmission line compensation distance relay functioning power swing stability nonlinear behaviour metal-oxide varistor phase current second harmonic analysis

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Multi-Resolution-Analysis-based Line-to-Ground fault detection in a VSC-Based HVDC System

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IETE JOURNAL OF RESEARCH 2020年第4期66卷 491-504页

作者： Kar Ray, Debopoma Chattopadhyay, Surajit Sengupta, Samarjit MCKV Inst Engn Dept Elect Engn Howrah India Ghani Khan Chowdhury Inst Engn & Technol Dept Elect Engn Malda India Univ Calcutta Dept Appl Phys Kolkata India

This paper deals with the identification of single line-to-ground (LG) fault in a voltage source controller based high voltage direct current (HVDC) link, monitoring the multi-resolution-analysis (MRA)-based discrete wavelet transform (DWT) coefficients. A prototype model has been developed to assess the system at normal and at LG fault at the receiving end of the HVDC link, monitoring the AC sending end current in the system. This study also deals with the assessment of locations of LG faults in the HVDC link at different positions, from the sending end. Case studies have been carried out using field data and different DWT-based statistical parameters have been estimated and their variations due to fault have been observed. Then DWT-level-based optimization and parametric optimization have been done. An algorithm has been developed for fault assessment. Based on the specific outcome of the case studies, distance relaying scheme has been proposed, which has been validated with software simulation based on real field data.

关键词： fault detection algorithm Field data Level optimization LG fault MRA-based distance relaying Parametric optimization Real-time digital simulation VSC-based HVDC system

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fault detection Method of Photovoltaic System Based on Power Change of Array 21

Fault Detection Method of Photovoltaic System Based on Power...

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Proceedings of the 2021 4th International Conference on algorithms, Computing and Artificial Intelligence

作者： Chao Tan Chenxi Li Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University China

ISBN: (纸本)9781450385053

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant change. Therefore, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power, and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified.

关键词： Sensorless Electrical data Power characteristics Photovoltaic System (PVS) fault detection algorithm

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Performance Monitoring algorithm for detection of Encapsulation Failures and Cell Corrosion in PV Modules

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ENERGIES 2023年第8期16卷 3391页

作者： Joseph, Easter Kumar, Pradeep Menon Vijaya Singh, Balbir Singh Mahinder Ching, Dennis Ling Chuan Univ Teknol PETRONAS Dept Fundamental & Appl Sci Seri Iskandar 32610 Perak Darul Rid Malaysia

This research work aims to develop a fault detection and performance monitoring system for a photovoltaic (PV) system that can detect and communicate errors to the user. The proposed system uses real-time data from various sensors to identify performance problems and faults in the PV system, particularly for encapsulation failure and module corrosion. The system incorporates a user interface that operates on a micro-computer utilizing Python software to show the detected errors from the PV miniature scale system. fault detection is achieved by comparing the One-diode model with a controlled state retrieved through field testing. A database is generated by the system based on acceptable training data and it serves as a reference point for detecting faults. The user is notified of any deviations based on the threshold value from the training data as an indication of an error by the system. The system offers real-time monitoring, easy-to-understand error messages, and remote access capability, making it an efficient and effective tool for both users and maintenance personnel to manage and maintain the PV system.

关键词： PV system PV monitoring fault detection algorithm module corrosion encapsulation failure

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fault detection Method of Photovoltaic System Based on Power Change of Array

Fault Detection Method of Photovoltaic System Based on Power...

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2021年第四届算法、计算和人工智能国际会议

作者： Chao Tan Chenxi Li Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast University

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant ***, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power,and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified.

关键词： Photovoltaic System(PVS) fault detection algorithm Electrical data Sensorless Power characteristics

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A Platform for fault Diagnosis of High-Speed Train based on Big Data

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IFAC-PapersOnLine 2018年第18期51卷 309-314页

作者： Xu, Quan Zhang, Peng Liu, Wenqin Liu, Qiang Liu, Changxin Wang, Liangyong Toprac, Anthony Joe Qin, S. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States

High-speed trains are very fast (e.g. 350km/h) and operate at high traffic density, so once a fault has occurred, the consequences are disastrous. In order to better control the train operational status by timely and rapid detection of faults, we need new methods to handle and analyze the huge volumes of high-speed railway data. In this paper, we propose a novel framework and platform for high-speed train fault diagnosis based on big data technologies. The framework aims to allow researchers to focus on fault detection algorithm development and on-line application, with all the complexities of big data import, storage, management, and realtime use handled transparently by the framework. The framework uses a combination of cloud computing and edge computing and a two-level architecture that handles the massive data of train operations. The platform uses Hadoop as its basic framework and combines HDFS, HBase, Redis and MySQL database as the data storage framework. A lossless data compression method is presented to reduce the data storage space and improve data storage efficiency. In order to support various types of data analysis tasks for fault diagnosis and prognosis, the framework integrates online computation, off-line computation, stream computation, real-time computation and so on. Moreover, the platform provides fault diagnosis and prognosis as services to users and a simple case study is given to further illustrate how the basic functions of the platform are implemented. © 2018

关键词： fault detection Big data Cloud computing Digital storage Edge computing Electric fault currents Failure analysis Information management Railroad cars Railroad plant and structures Railroad transportation Railroads Speed Big data technologies Diagnosis and prognosis fault detection algorithm Framework and platform High speed railways High speed train (HST) Lossless data compression method Real time computations

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Data-Driven Approach for Feature Drift detection in Embedded Electronic Devices ⁎

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IFAC-PapersOnLine 2018年第24期51卷 1024-1029页

作者： Djedidi, Oussama Djeziri, Mohand A. M'Sirdi, Nacer K. Aix Marseille University Université de Toulon CNRS LIS SASV Marseille France

This paper is a part of a project aiming to develop supervisor and monitoring devices for embedded systems in airplanes and vehicles. It focuses on the reliability of these systems and establishes a monitoring framework to detect drifts and faults in the behavior of the heterogeneous central processing units (CPU) and graphics processing units (GPU) chips powering them. In this work, we use a previously developed incremental model of these chips and associate it with a fault detection algorithm. Estimations from the model constitute inputs to the diagnosis module. The latter generates alarms in the presence of faults or drifts in the characteristics and features of the System-on-Chip (SoC). The obtained results validate the proposed monitoring algorithm and demonstrate the effectiveness of the fault detection algorithm. © 2018

关键词： Monitoring Computer graphics Embedded systems fault detection Graphics processing unit Identification (control systems) Microprocessor chips Models Program processors Programmable logic controllers Signal detection System on chip Analytical redundancy Embedded electronic devices fault detection algorithm Graphics Processing Unit (GPU) Incremental modeling Monitoring algorithms Monitoring frameworks System on chips (SoC)

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Analytics of Heterogeneous Process Data: Multiphase Flow Facility Case Study

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IFAC-PapersOnLine 2018年第18期51卷 363-368页

作者： Stief, Anna Tan, Ruomu Cao, Yi Ottewill, James R. ABB Corporate Research Center ul. Starowislna 13a Krakow31-038 Poland School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China

Improvements in sensing, connectivity and computing technologies mean that industrial processes now generate a vast amount of data from a variety of disparate sources. Data may take a number of different forms, from different time-domain signals, sampled at different rates using various types of sensors, through to more disparate sources such as alarm and event logs. New process and condition monitoring techniques are needed to be developed to tackle the new challenges of big and heterogeneous data. Although there are a few publicly available benchmark studies, e.g. the Tennessee Eastman process plant (Ricker, 1995), a multiphase flow benchmark case for statistical process monitoring (Ruiz-Cárcel et al., 2015), they provide only standard process data. This work presents a benchmark case on an industrial scale multiphase flow facility. Various operational conditions were tested under normal operating modes as well as with seeded faults. Heterogeneous data was collected from various sources, including process data, alarm data and high frequency ultrasonic and pressure data. Two different fault detection algorithms are applied to the data, a multivariate PCA-enhanced Canonical Variate Analysis (CVA) and a probabilistic Bayesian method. This benchmark case study with data from disparate sources can be used for algorithm development and validation for fault detection, fault identification, fault classification, fault severity detection, monitoring of fault evolution and prognostics. © 2018

关键词： fault detection Bayesian networks Condition monitoring Data Analytics Experiments Multiphase flow Process monitoring Statistical process control Time domain analysis Ultrasonic applications algorithm development Benchmark case studies Canonical variate analysis fault detection algorithm Operational conditions Process and condition monitoring Statistical process monitoring Tennessee Eastman process

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Active fault tolerant control applied to REPOINT, a novel railway track switch

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IFAC-PapersOnLine 2018年第24期51卷 529-535页

作者： Kaijuka, Precious Dixon, Roger Ward, Christopher Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Epinal WayLoughboroughLE11 3TH United Kingdom School of Engineering Birmingham University BirminghamB15 2TT United Kingdom

Railway networks are fitted with switches and crossings that enable trains to move from one track to another however they present a single point of failure. Existing track switches actuation is performed in the open loop presenting a research gap where closed-loop fault tolerant control can be applied to track switch actuation in order to improve railway network performance. A new railway track switch, REPOINT has been developed at Loughborough University with a new electromechanical design that incorporates actuator redundancy to improve the reliability of track switch operation. This paper looks at the development and validation of a sensor fault detection, identfication and accomodation scheme applied to a detailed non-linear model representing the laboratory scale demonstrator of the REPOINT concept. A residual-based fault diagnosis scheme is developed from the comparison of estimates generated by a bank of observers and output measurements. In the presence of sensor faults, a reconstructed signal from the fault detection algorithm is used to replace the measured signal for feedback control and thus safe switching position control is achieved. The results demonstrate that using a reliable fault tolerant control configuration could increase the availability and reliability of the REPOINT track switch. © 2018

关键词： fault detection Applications fault tolerance fault tolerant computer systems Position control Railroad track switches Railroads Redundancy Active fault tolerant control Electro mechanical design fault accommodation fault detection algorithm fault tolerant control Loughborough University Reconfigurable control Reconfiguration strategy

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