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Robustification of fault detection algorithm in a three-phase induction motor using MCSA for various single and multiple faults

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IET ELECTRIC POWER APPLICATIONS 2021年第5期15卷 593-615页

作者： Kompella, K. C. Deekshit Rongala, Naga Sreenivasu Rayapudi, Srinivasa Rao Mannam, Venu Gopala Rao SNIST EEE Dept Hyderabad India IRSEE Southern Railways Tamil Nadu India JNTUK Dept Elect Engn Kakinada India PVPSIT Dept EEE Vijayawada India

fault diagnosis in induction machines, particularly at a premature stage, has become necessary to avoid unexpected interruption of the industrial process. Moreover, a reliable and cost-effective fault detection process is highly essential to avoid inappropriate shut down of the machine. Thus, a critical comparison is done between two popular pre-fault component cancellation techniques and a reliability test is performed to develop a robust algorithm for fault diagnosis in three-phase induction motor. To achieve this, various single and multiple faults experienced by induction motor are created and tested to examine the effectiveness of developed algorithm. Evaluation of the proposed topology based on motor current signature analysis is done by repeating the process for several times and tested for consistency. As a part of this, various feature extraction parameters are computed and compared to identify the best estimate of fault and its seriousness. The proposed technique is examined in MATLAB and LabVIEW environment and experiments on a three-phase, 1.5-kW induction motor.

关键词： fault diagnosis popular pre-fault component cancellation techniques reliable cost-effective fault detection process industrial process power 1.5 kW premature stage robust algorithm induction motors machine testing induction machines motor current signature analysis feature extraction Asynchronous machines fault detection algorithm Reliability reliability test single faults MCSA reliability three-phase induction motor

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New fault detection algorithm for an Improved Dual VSM Control Structure With FRT Capability

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IEEE ACCESS 2021年 9卷 125134-125150页

作者： Abdelrahim, Ahmed Smailes, Michael Ahmed, Khaled H. Mckeever, Paul Egea-Alvarez, Agusti Univ Strathclyde Elect & Elect Engn Dept Glasgow G1 1XW Lanark Scotland Arab Acad Sci Technol & Maritime Transport Elect & Control Dept Alexandria 1029 Egypt Natl Renewable Energy Ctr Offshore Renewable Energy Catapult Blyth NE24 3AG England

Grid forming converters are a promising solution to enhance the stability of electrical networks with a high penetration of renewable generation. Virtual Synchronous Machines (VSM) are a particular type of grid forming converter, which can be implemented without an inner Current Control (CC) loop. This VSM implementation can provide a better inertial response than other grid forming structures with an inner CC. However, it cannot limit the current through the converter during a fault. A standard solution uses a VSM as the primary controller with a conventional inner CC acting as a backup controller during fault conditions, referred to here as a Dual VSM control structure. The switching action between the primary and the backup controllers is dependent on an accurate fault detection algorithm (FDA). The conventional FDA mentioned in the literature has limitations in particular but not uncommon scenarios. The article shows the limitations of the conventional FDA in weak grids and unbalanced conditions and introduces a new FDA with improved performance. Two types of sensitivity analysis are used to study the dynamics of the proposed control approach. The First is the sensitivity of the proposed control structure to different fault locations in strong and weak grids. The Second is the sensitivity of the controller response to changing controller parameters. The second analysis is introduced as a reference for tuning and improvement of the control structure introduced.

关键词： Switches Sensitivity analysis Voltage control Tuning fault detection Damping Current control Balanced faults current control fault detection algorithm grid forming converters grid following converters parameter sensitivity analysis unbalanced faults virtual synchronous machine weak grids

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S-Transform based fault detection algorithm for enhancing distance protection performance

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 130卷 106966-106966页

作者： Chavez, Jose J. Popov, Marjan Lopez, David Azizi, Sadegh Terzija, Vladimir Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands Red Elect Espana Secur Syst Dept Alcobendas 28109 Madrid Spain Univ Leeds Leeds LS2 9JT W Yorkshire England Skolkovo Inst Sci & Technol Skoltech Ctr Energy Sci & Technol CEST Bolshoy Blvd 30Bld 1 Moscow Russia

This paper presents a new fault detection algorithm based on the Fast Discrete Stockwell Transform. The algorithm can improve the functionality of existing distance protection and resolve shortcomings identified during the fault detection process in case of fault occurrence in systems with a high penetration of power electronicsbased generators. Reported results of the operation of commercial distance relays of four different vendors show that all relays experience difficulties during ungrounded faults. An RTDS testbed is developed for extensive hardware in the loop testing, comprising electromagnetic transient models of Type-3 and Type-4 wind generators. The proposed algorithm successfully overcomes the identified problems for cases where commercial relays maloperate. The threshold parameters for the fault detection are set by using the energy content attributed to the Fast Discrete Stockwell Transform time?frequency domain signal. Other distance protection modules such as the determination of directionality, phase selection and the computation of the impedance, which are necessary for the protection selectivity, are developed based on currently available solutions applied in commercial relays. The new algorithm has been extensively tested using RTDS for various fault conditions and the obtained results are reported in the paper.

关键词： fault detection algorithm Distance protection Testing RTDS

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fault detection algorithm used in a magnetic monitoring system of the hydrogenerator

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IET ELECTRIC POWER APPLICATIONS 2017年第1期11卷 63-71页

作者： Babic, Blagoje M. Milic, Sasa D. Rakic, Aleksandar Z. Univ Belgrade Sch Elect Engn Signals & Syst Dept Bulevar Kralja Aleksandra 73 Belgrade Serbia Univ Belgrade Elect Engn Inst Nikola Tesla Dept Elect Measurements Koste Glavinica 8a Belgrade Serbia

In this study, the authors propose a fault detection algorithm used in an on-line magnetic monitoring system in the hydropower plant. The proposed algorithm is based on two magnetic measuring methods: measurement of (inner) flux within generator in air gap and measurement of stator leakage (outer) flux outside of the generator. The system has to ensure, in situ and real time, magnetic monitoring and fault detection of hydrogenerator. The monitoring system is also useful for modern maintenance approach such as a condition based maintenance without generators' work interruption and better planned and preventive maintenance in the power plant. The proposed system successfully detects the presence of magnetic unbalance of hydrogenerator, occurring as a consequence of shorted turns in the windings of the rotor poles or air gap asymmetry. The presented measurement results are achieved in real exploitation conditions.

关键词： fault diagnosis hydroelectric generators preventive maintenance machine insulation fault detection algorithm magnetic monitoring system hydrogenerator magnetic measuring methods stator leakage flux measurement condition based maintenance preventive maintenance power plant magnetic unbalance rotor poles air gap asymmetry

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fault detection algorithm for Multiple GCPV Array Configurations

Fault Detection Algorithm for Multiple GCPV Array Configurat...

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IEEE Manchester PowerTech

作者： Dhimish, Mahmoud Holmes, Violeta Dales, Mark Mather, Peter Sibley, Martin Chong, Benjamin Zhang, Li Univ Huddersfield Dept Comp & Engn Huddersfield W Yorkshire England Univ Leeds Sch Elect & Elect Engn Leeds W Yorkshire England

ISBN: (纸本)9781509042371

In this paper, a fault detection algorithm for multiple grid-connected photovoltaic (GCPV) array configurations is introduced. For a given set of conditions such as solar irradiance and photovoltaic module temperature, a number of attributes such as power, voltage and current are calculated using a mathematical simulation model. Virtual instrumentation (VI) LabVIEW software is used to monitor the performance of the GCPV system and to simulate the theoretical I-V and P-V curves of the examined system. The fault detection algorithm is evaluated on multiple GCPV array configurations such as series, parallel and series-parallel array configuration. The fault detection algorithm has been validated using 1.98 kWp GCPV system installed at the University of Huddersfield. The results indicates that the algorithm is capable to detect multiple faults in the examined GCPV plant and can therefore be used in large GCPV installations.

关键词： Photovoltaic (PV) fault detection algorithm GCPV multiple array configurations VI LabVIEW software

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fault detection algorithm for grid-connected photovoltaic plants

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SOLAR ENERGY 2016年 137卷 236-245页

作者： Dhimish, Mahmoud Holmes, Violeta Univ Huddersfield Dept Comp & Engn Huddersfield W Yorkshire England

This paper presents detailed procedure for automatic fault detection and diagnosis of possible faults occurring in a grid-connected photovoltaic (GCPV) plant using statistical methods. The approach has been validated using an experimental data of climate and electrical parameters based on a 1.98 kWp plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this paper is, therefore, to create a system capable of simulating the theoretical performances of PV systems and to enable statistical analysis of PV measured data. The fault detection algorithm compares the measured and theoretical output power using statistical t-test. In order to determine the location of the fault, the ratio between the measured and theoretical DC power and voltage is monitored. The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults. Some of the typical faults are fault in a photovoltaic module, photovoltaic string and faulty maximum power point tracker (MPPT) unit. A virtual instrumentation (VI) LabVIEW software was used in the system development and implementation. This system was used successfully for fault detection on the GCPV plant. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Grid-connected photovoltaic plant fault detection algorithm Statistical analysis LabVIEW

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Enhancing reliability and lifespan of PEM fuel cells through neural network-based fault detection and classification

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第41期48卷 15612-15625页

作者： Dhimish, Mahmoud Zhao, Xing Univ York Sch Phys Engn & Technol Renewable Energy Lab York YO10 5DD North Yorkshire England

In order to maximise fuel cell reliability of operation and useful life span, an accurate online health assessment of the fuel cell system is essential. Existing algorithms for fault detection in fuel cell systems are based on sensing elements, control methods, and sta-tistical/probabilistic models. In this paper, an artificial neural network (ANN) will be developed to detect and classify faults in proton-exchange membrane (PEM) fuel cell sys-tems. As the ANN model developed within the PEM system relies on the input and output current and voltage, additional sensing devices are not required within the system. Based on an experimental setup using a 3-kW fuel cell system, it was found that the proposed model was able to detect faults associated with the reduction/increase of fuel pressure, H2 consumption rate, and voltage regulation changes in the dc-dc converter with >90% ac-curacy. In the proposed model, historical data is required to train and validate the ANN algorithm, but after this is complete, no human intervention is required afterward.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Fuel cell system fault detection algorithm fault classification Artificial neural networks (ANN) Machine learning algorithm

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Artificial Intelligence Applications in Water Treatment and Desalination: A Comprehensive Review

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WATER 2025年第8期17卷 1169-1169页

作者： Alenezi, Anwur Alabaiadly, Yousef Kuwait Inst Sci Res Water Res Ctr Safat 13109 Kuwait Minist Elect & Water Studies & Res Dept Kuwait 12010 Kuwait

This paper examines the application of artificial intelligence (AI) in desalination. The study explored AI techniques, including machine learning, neural networks, and genetic algorithms, to enhance system efficiency and reduce energy costs. Case studies assessed the impact of AI on desalination systems, including those powered by renewable energy sources. Key findings revealed that AI-driven systems improved water quality, reduced energy consumption by up to 50%, and enabled predictive maintenance, minimizing downtime. Challenges in integrating AI with renewable energy-powered water treatment and desalination systems were addressed by analyzing hybrid setups combining solar, wind, and battery storage with reverse osmosis (RO) and multi-stage flash (MSF) technologies. These systems demonstrated critical improvements in energy efficiency, making desalination more viable for arid and remote areas. fault detection algorithms and predictive maintenance emerged as pivotal AI applications, significantly reducing maintenance costs and enhancing reliability. This study was concluded by identifying challenges such as the intermittent nature of renewable energy and the complexities of designing scalable hybrid AI systems. Future research should further refine AI techniques, advancing sustainable and energy-efficient water treatment solutions.

关键词： artificial intelligence desalination technologies water treatment renewable energy integration process optimization fault detection algorithm machine learning applications predictive maintenance energy efficiency hybrid AI systems

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fault detection for Photovoltaic Systems Using Multivariate Analysis With Electrical and Environmental Variables

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IEEE JOURNAL OF PHOTOVOLTAICS 2021年第1期11卷 202-212页

作者： Kim, Gyu Gwang Lee, Wonbin Bhang, Byeong Gwan Choi, Jin Ho Ahn, Hyung-Keun Konkuk Univ Next Generat Photovolta Module & Power Syst Res C Seoul 05029 South Korea

fault detection and repair of the components of photovoltaic (PV) systems are essential to avoid economic losses and facility accidents, thereby ensuring reliable and safe systems. This article presents a method to detect faults in a PV system based on power ratio (PR), voltage ratio (VR), and current ratio (IR). The lower control limit (LCL) and upper control limit (UCL) of each ratio were defined using the data of a test site system under normal operating conditions. If PR exceeded the set range, the algorithm considered a fault. Subsequently, PR and IR were examined via the algorithm to diagnose faults in the system as series, parallel, or total faults. The results showed that PR exceeded the designated range between LCL (0.93) and UCL (1.02) by dropping to 0.91-0.68, 0.88-0.62, and 0.66-0.33 for series, total, and parallel faults, respectively. Moreover, VR exceeded the LCL (0.99) and UCL (1.01) by 0.95-0.69 and 0.91-0.62 for series and total faults, respectively, but not under parallel faults condition. IR did not change in series and total faults but exceeded the range of LCL (0.93) and UCL (1.05) by dropping to 0.66-0.33. Thus, faults in PV systems can be detected and diagnosed by analyzing quantitative output values.

关键词： Resistance Correlation Photovoltaic systems Radiation effects fault detection Voltage measurement Supervised learning Correlation coefficient current ratio electrical variables fault detection algorithm multivariate analysis power ratio regression analysis voltage ratio weather variables

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Photovoltaic Bypass Diode fault detection Using Artificial Neural Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Dhimish, Mahmoud Tyrrell, Andy M. Univ York Sch Phys Engn & Technol York YO10 5DD England

Due to the importance of determining faulty bypass diodes in photovoltaic (PV) systems, faulty bypass diodes have been of widespread interest in recent years due to their importance in improving PV system durability, operation, and overall safety. This article presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANNs) to detect short and open PV bypass diode fault conditions. With only three inputs from the PV system, namely, the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.

关键词： Circuit faults Artificial neural networks Photovoltaic systems Temperature measurement Photovoltaic cells Neurons fault diagnosis Artificial intelligence (AI) bypass diodes fault detection algorithm photovoltaics (PVs)

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