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Abstract: This paper presents a high speed, computationally efficient scheme for protection of transmission lines. The relay logic consists of three parts: directional protection, fault classification and fault location. Wavelet transform is used for extracting information from the fault transients and only the first level high frequency details of the voltages and currents are used. Proposed protection logic compares the directional signals from both terminals to discriminate between faults inside and outside the zone of interest. Fault classification is achieved using local terminal current information. An estimate of the location of the faults is obtained utilizing single faulted phase current information from both terminals. The logic is deterministic and can work reliably in the presence of fault resistance, load variation and CT saturation. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a four bus meshed system modeled in EMTP/ATP. [Copyright &y& Elsevier]

In this paper, a novel virtual-instrument-based monitoring and diagnosis technique that permits the identification of rotor faults in small inverter-fed induction motors is presented and discussed. Since it allows the continuous monitoring of the machine, it is a useful tool for the implementation of advanced maintenance strategies. Through a proper experimental setup, the performance, repeatability, and portability of the proposed algorithm have been evaluated. [ABSTRACT FROM AUTHOR]

This paper presents a fast hardware-efficient logic for fault detection and classification in transmission lines, implemented using a field-programmable gate array (FPGA). The general-purpose SPARTAN3E FPGA was employed for developing the prototype, with all the coding done using a hardware description language (HDL) called very high speed integrated circuit (VHDL). The proposed logic employs only one-terminal current samples and is based on wavelet analysis. Depending on the amount of high frequency components in the current signals after processing, the faults are classified into ten types. The Real Time Windows Target Toolbox of MATLAB was used to apply the current signal inputs to the prototype in real time. An adaptive threshold value is chosen, rather than a fixed threshold in the case of faults involving the ground, to make the classification reliable and accurate. The fault classification time is 6 ms, which is about 1/3 of the power frequency cycle (20 ms). A high level of computational efficiency is achieved as compared to the other wavelet-transform-based algorithms, since only the high frequency details at first level are employed in this algorithm. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a system modeled in the Electromagnetic Transients Program/Alternative Transients Program. The proposed logic was found to be highly reliable and accurate, even in the presence of fault resistance. [ABSTRACT FROM AUTHOR]

Accurate fault classification and swift faulted-phase selection are the bases of protection for transmission systems. This paper presents the algorithm of fault classification and faulted. phase selection based on the initial current traveling wave. The characteristics of various faults are investigated on the basis of the Karenbauer transform. The criteria of fault classification and faulted-phase selection are put forward. In order to extract the traveling wave from postfault signals and to construct the algorithm, wavelet transform technology is adopted. The simulations based on the electromagnetic transients program have been done to verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Use of a novel phase selector algorithm for fault classification on half wavelength lines. • The new algorithm was coded in a RTDS hardware, emulating a relay. It use the derivate of Clarke's components for detect the type fault. • Identification of misoperations from relay commercial, when used to protection on half wavelength lines. • Robustness of proposed algorithm to identify faults with high impedance, conditions of transients caused by energization and tree phase auto recloser of half wavelength lines. • Use of COMTRADE files for the analysis and evaluation of the phase selector by the different types of faults. This study presents a new phase selector algorithm and fault type classification for very long transmission lines, known as the half-wavelength lines (HWL). For this work, the Clarke components variation rate method applied to current magnitudes was used. The method was implemented in a real-time digital simulation hardware-in-the-loop (HIL) test environment. We applied different fault types at each 5% line length, obtaining Clarke component current phasors. The proposed algorithm is stable for the native behavior of the half-wavelength line, including the resonance zones and their mitigation procedures. In addition, the algorithm is immune to the fault insertion angle, fault resistance, and line loading level. Finally, the algorithm could be applied in commercial microprocessor-based relays or could be used as a post-fault analysis tool. [ABSTRACT FROM AUTHOR]

ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor ( KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification. [ABSTRACT FROM PUBLISHER]

Worm gearboxes (WG) are frequently used in many areas of the industry. WG is different from other gearbox types and due to their working principles they are under high risk of wear and fault. Therefore, detection of faults that may occur in WG and taking measures accordingly are especially important for systems and facilities that require uninterrupted operation. This paper introduces an intelligent feature selection and classification method for fault diagnosis of WGs under different working conditions. The novelty of the study lies in the selection of feature sources and different loading and speed conditions for condition monitoring studies of WGs experimentally. Fault detection and classification were performed based on vibration, sound and thermal images data which were acquired and processed from the healthy and the faulty WG. Apart from classical studies, time and frequency domain features and thermal images features were extracted and evaluated singularly, dual or triple forms with ANN (Artificial Neural Network) and SVM (Support Vector Machines) classifiers. Reasonable classification performances for fault detection were observed when the features of all three sources used (99.2% with ANN and 98.7% with SVM). ANN and SVM classification performances are almost equal for fault classification (98.9% with SVM and 98.6% with ANN). The findings of the study would be a possible means for online condition monitoring of industrial plants, conveyors, elevators, etc. [ABSTRACT FROM AUTHOR]

• In this paper combined DWT and Fuzzy inference system has been utilized for fault detection and classification in distribution system integrated with DGs. • The proposed scheme is suitable for detection of both HIF and LIF. • The proposed scheme based on DWT and Fuzzy inference system has been tested for IEEE 13 Bus system and IEEE 33 bus system. • It considers the effects of various DGs, switching events and impact of noise. • The scheme has been able to distinguish between different conditions such as transformer energization, capacitor bank switching, load switching, evolving faults. Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions. [ABSTRACT FROM AUTHOR]

Insulators are components of electrical power grid that have the function of mechanically supporting cables and isolating electrical potential. The proper functioning of the insulators is essential for the continuity in the supply of electrical energy. When an insulator has its properties damaged, disruptive discharges may shut down the system and impairs the network's reliability. For this reason, classifying adverse conditions is a critical task to keep the system running. In this paper, the echo state network is used for the classification of the insulators based on the ultrasound signal. Hypertuning is applied to automate the evaluation of the parameters and optimize the network to have a general application. The insulators are evaluated in the laboratory under controlled conditions from the application of 7.95 kV (phase-to-ground), under the same conditions that are found in the field. The assessment is made on perforated and contaminated insulators, which were removed from service due to defects. The echo state network achieves 87.36 % accuracy for the multiclassification and 99.99 % for the specific classification of drilling. For comparative analysis, the multilayer perceptron and support-vector machines are evaluated based on the fast fourier transform. The results show that echo state network is promising to classify the evaluated conditions, being more accurate than the multilayer perceptron and support-vector machines based on fast fourier transform. [ABSTRACT FROM AUTHOR]

Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT fault diagnosis. To achieve high classification accuracy and reduced computation time, a fault diagnosis framework with a combination of the AC-side three-phase current, and the upper and lower bridges' currents of the MMCs to automatically classify health conditions of MMCs is proposed. In this framework, the principal component analysis (PCA) is used for feature extraction. Then, two classification algorithms—multiclass support vector machine (SVM) based on error-correcting output codes (ECOC) and multinomial logistic regression (MLR)—are used for classification. The effectiveness of the proposed framework is validated by a two-terminal simulation model of the MMC-high-voltage direct current (HVDC) transmission power system using PSCAD/EMTDC software. The simulation results demonstrate that the proposed framework is highly effective in diagnosing the health conditions of MMCs compared to recently published results. [ABSTRACT FROM AUTHOR]

• Sensitivity analysis of different transfer function components for different faults. • Correlation coefficient drawbacks investigation on fault detection. • Proposing a new index for the classification of different faults. • Investigation of fault classification accuracy of transfer function components. • Detection and classification of fault type, location and extent of faults. Frequency response analysis is one of the best methods to diagnose mechanical defects in the power transformer winding. In this paper, sensitivity analysis of four components of the transfer function (TF), i.e., amplitude, phase, real and imaginary components of TF is investigated for different defects. The classification of type, location, and extent of different defects is carried out using support vector machine with cubic polynomial kernel. The accuracy of different indices and four different components of TF are investigated. Moreover, a new index based on fitting performance is defined. The results show that despite widespread use and popularity of amplitude of TF, the phase, imaginary, and real components of TF, have more sensitivity to the defects respectively and are more accurate for classification. Also, compared with the results of some high usage indices, the proposed index has the best classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Malfunctions in machinery are often sources of reduced productivity and increased maintenance costs in various industrial applications. For this reason, machine condition monitoring is being pursued to recognise incipient faults. In this paper, the fault diagnostic problem is tackled within a neuro-fuzzy approach to pattern classification. Besides the primary purpose of a high rate of correct classification, the proposed neuro-fuzzy approach also aims at obtaining an easily interpretable classification model. The efficiency of the approach is verified with respect to a literature problem and then applied to a case of motor bearing fault classification. [Copyright &y& Elsevier]

Condition monitoring of machine is recognized as effective strategy for undertaking the maintenance in wide variety of industries. Planetary gearbox is a critical component in helicopters, wind turbines, hybrid vehicles and so forth. Planetary gearbox are complex in nature due to its size and meshing components. Condition monitoring and fault diagnosis of planetary gearbox is challenging due to complexity in dependable fault extraction from raw vibration signal. The mechanism of planetary gearbox is complex as there are several gears meshing at the same time. To find out the nature of fault and defective component in planetary gearbox is difficult. In this paper, the fault detection and fault type identification diagnostic approach using auto regression model (AR) and continuous wavelet transforms (CWT) by considering different frequency range is established. The experimental research conducted with different type of fault vibration signals in the gearbox have been diagnosed and identified the fault type using AR Modelling, Impulse and Shape Factor for validation purposes. The unique behaviors and fault characteristics of planetary gearboxes are identified and analyzed. The fault frequency identification and extraction of features from the non-stationary signals in different fault severity level of vibration data demonstrates the reliability of proposed method. The developed algorithm adds efficacy in detecting the nature of fault and defective component without performing a visual inspection. [ABSTRACT FROM AUTHOR]

• A new fault classification approach is presented for series-compensated line. • One-end current-based fault location method is proposed. • Different series-capacitor compensation configurations are analyzed. • The scheme is economic, reliable, fast, and generalized. This paper proposes a new efficient method for fault detection, faulty phase selection, and fault point identification on series capacitor-compensated transmission line. The method is based on sensing only the three phase current at the relay site. A reverse synchronous reference frame (RSRF) approach is proposed for fast fault detection and classification. Then, an analytical algorithm based on symmetrical components theory is presented for accurate estimation of fault location. Three possible arrangements of the series capacitor bank (SCB) on the line are analyzed. The SCB and the associated protective metal oxide surge arrestor (MOV) are modeled thoroughly to characterize its behavior during fault. In addition to its simplicity and practicality, the proposed scheme is equipped with decaying DC offset removal technique that allows fast extraction of the current phasors within one fundamental cycle. The proposed method works well for all ground and non-ground faults; it does not need pre-fault information; does not require any communication facilities; avoids high voltage measurement; and works smoothly for a wide range of fault resistance values. The efficacy of the proposed method is proved by a large number of simulated cases under a variety of operating conditions. [ABSTRACT FROM AUTHOR]

The Open Circuit (OC) faults occurring in switches of Multilevel Converters (MLC) may lead to undesirable operation of the converter. Therefore, fault detection and its localization in minimum time are necessary. This paper focuses on the fast fault detection algorithm based on the two samples technique and the fault localization algorithm using the Entropy of Wavelet Packets (EWP) as a feature. The EWP feature is used to classify and localize the OC faults in Insulated Gate Bipolar Transistors (IGBTs) of three-phase, three-level inverter using Support Vector Machine (SVM) based fault classification algorithm. The proposed technique can detect the fault in single IGBT and multiple IGBTs in a lesser time range of microseconds to 0.33 ms. It gives better performance and accuracy (99.70%) than previously proposed SVM algorithms, as the EWP-based feature extraction process used in this paper is simple and accurate with a less computational burden. [Display omitted] • An observer-based algorithm is proposed using two samples-based technique. • The Entropy of Wavelet Packets (EWP-SVM) technique is used for fault diagnosis. • The proposed algorithm can detect the faults in a single IGBT and multiple IGBTs. • The technique is faster and accurate than techniques available in the literature. • The comparison of detection time of different techniques is also discussed. [ABSTRACT FROM AUTHOR]

In this paper, a novel fault detection and classification method for photovoltaic (PV) arrays is introduced. The method has been developed using a dataset of voltage and current measurements (I–V curves) which were collected from a small-scale PV system at the RELab, the University of Jijel (Algeria). Two different machine learning-based algorithms have been used in order to detect and classify the faults. An Internet of Things-based application has been used in order to send data to the cloud, while the machine learning codes have been run on a Raspberry Pi 4. A webpage which shows the results and informs the user about the state of the PV array has also been developed. The results show the ability and the feasibility of the developed method, which detects and classifies a number of faults and anomalies (e.g., the accumulation of dust on the PV module surface, permanent shading, the disconnection of a PV module, and the presence of a short-circuited bypass diode in a PV module) with a pretty good accuracy (98% for detection and 96% classification). [ABSTRACT FROM AUTHOR]

A new technique for automated detection and diagnosis of rolling bearing conditions is proposed. The time-domain vibration signals of rolling bearing with different fault conditions are processed using La place wavelet transform for features extraction. The extracted features in time and frequency domain of the wavelet transform coefficients are applied as input vectors to artificial neural networks (ANN) for rolling bearing fault classification. The Laplace-Wavelet shape and the ANN classifier parameters are optimized using a genetic algorithm. To speed up the computation and increase the accuracy of classification process the predominate wavelet transform scales are used for features extraction. The results show the effectiveness of the proposed technique for bearing conditions identification with very high show rate using minimum input features. [ABSTRACT FROM AUTHOR]

Quick and accurate identification of the root cause of failures is an important prerequisite for any reliable system. However with increasing Grid size and complexity, the manual diagnosis of application faults becomes impractical, tedious and time-consuming. So far there has been a lack of systematic and comprehensive studies in organization and classification of Grid faults. We address this gap with a multi-perspective Grid fault taxonomy precisely describing an incident using eight different characteristics. Based on that, we develop a pragmatic model-based technique for application-specific fault diagnosis using indicators, symptoms and rules. Customized wrapper services then apply this knowledge to reason about the root causes of failures. In addition to applying user-provided diagnosis models we demonstrate that given a set of past classified fault events it is possible to automatically extract new models through learning. We investigated and compared several supervised classification learning and cluster analysis algorithms for this purpose. Our approach was implemented as part of the Otho Toolkit, a framework for "service-enabling" legacy applications by synthesizing tailor-made wrapper service. [ABSTRACT FROM AUTHOR]

This article presents a fast, hardware efficient logic for fault detection and classification in transmission lines. The proposed logic employs only one-terminal current samples and is based on multi-resolution wavelet analysis. First-level high-frequency details of the modal currents in the range of 5 to 10 kHz are extracted. Depending on the amount of high-frequency components in the transformed current signals after processing, the faults are classified into ten types. An adaptive threshold value is chosen, rather than a fixed threshold in the case of faults involving the ground, to make the classification reliable and accurate. The validity of the proposed logic was exhaustively tested by simulating various types of faults on a system modeled in Alternative Transient Program (ATP)/Electromagnetic Transient Program (EMTP).The proposed logic was found to be acceptably reliable and accurate even in the presence of fault resistance and current transformer (CT) saturation. [ABSTRACT FROM AUTHOR]

A new on-line fault detection and isolation (FDI) scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and can be directly implemented in an on-board diagnosis system (hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model (MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

Series compensation at the midpoint of a transmission line creates problems to conventional protection approaches. A new fault classification technique has been developed for a transmission line with a series capacitor at the midpoint, having different percentages of compensation varying from 25% to 75%. The proposed technique requires three line currents and voltages at each end and computes fundamental phasors of these quantities using a modified version of Full Cycle Discrete Fourier Transform. Using these phasors, the absolute values of three phase impedances are calculated for faulted phase identification. Moreover, the involvement of the ground in the fault is identified using a zero sequence component of the fault current. Using a PSCAD/EMTDC software package, a large test data (28,800) set has been generated with different types of faults and system variables, which includes fault resistances, fault inception angles, fault positions (before and after series capacitor) and variable loading conditions along with wide variations in the source impedances at both ends of a transmission line. The proposed scheme is tested on the said data set and the results are found to be promising. The results indicate that the proposed technique is fast, accurate and robust for a wide variation in system and fault conditions. [ABSTRACT FROM AUTHOR]

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor. [ABSTRACT FROM AUTHOR]

The proposed algorithm for fault location, different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employs wavelet transform together with fuzzy inference system (FIS) and the adaptive neuro-fuzzy inference system (ANFIS) to incorporate expert evaluation so as to extract important features from wavelet multi-resolution analysis (MRA) coefficients for obtaining coherent conclusions regarding fault location. Simulation results indicate that both the classification and localization algorithms are immune to the effects of fault inception angle, impedance and distance. The most significant contribution of this article is that the proposed ANFIS approach has superiority over FIS for location of transmission line faults and thus can be used as an effective tool for real-time digital relaying purposes. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a real-time wavelet-Fuzzy combined approach for digital relaying. The algorithm for fault classification employs wavelet multi resolution analysis (MRA) to overcome the difficulties associated with conventional voltage and current based measurements due to effect of factors such as fault inception angle, fault impedance and fault distance. The proposed algorithm for fault location, different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employs wavelet transform together with fuzzy logic. The wavelet transform captures the dynamic characteristics of the non-stationary transient fault signals using wavelet MRA coefficients. The fuzzy logic is employed to incorporate expert evaluation through fuzzy inference system (FIS) so as to extract important features from wavelet MRA coefficients for obtaining coherent conclusions regarding fault location. Computer simulations using MATLAB have been conducted for a 300Km 400KV line. Simulation results indicate that both the classification and localization algorithms are immune from effects of faults inception angle, impedance and distance. The most significant contribution of this paper is that the proposed location algorithm has a maximum error of 6.5% with a computational time of about one cycle. Thus both classification and location algorithms can be used as effective tools for real-time digital relaying purpose. [Copyright &y& Elsevier]

Due to the importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time–frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The results that we achieved from wavelet analysis and SVM are fully in agreement with empirical result. [Copyright &y& Elsevier]

Abstract: This paper describes a new algorithm to identify the reflective waves for fault location in noisy environment. The new algorithm is based on the correlation of detail components at adjacent levels of stationary wavelet transform of current signal from one end of the cable. The algorithm is simple and straightforward. Simulation results based on a real power transmission system proved it can detect and locate the fault in very difficult situations. [Copyright &y& Elsevier]

Abstract: Genetic programming (GP) is a stochastic process for automatically generating computer programs. In this paper, three GP-based approaches for solving multi-class classification problems in roller bearing fault detection are proposed. Single-GP maps all the classes onto the one-dimensional GP output. Independent-GPs singles out each class separately by evolving a binary GP for each class independently. Bundled-GPs also has one binary GP for each class, but these GPs are evolved together with the aim of selecting as few features as possible. The classification results and the features each algorithm has selected are compared with genetic algorithm (GA) based approaches GA/ANN and GA/SVM. Experiments show that bundled-GPs is strong in feature selection while retaining high performance, which equals or outperforms the two previous GA-based approaches. [Copyright &y& Elsevier]

Abstract: An approach for classification of transmission line faults is presented. The approach is based on fuzzy logic and requires the consideration of the samples of three phase currents at one end of transmission line. To illustrate the effectiveness of the proposed approach extensive simulation studies, using EMTP and MATLAB, have been carried out for different types of fault considering wide variations in the operating conditions. [Copyright &y& Elsevier]

Two of the most expected objectives of transmission line protection are: 1) differentiating precisely the internal faults from external and 2) indicating exactly the fault type using one end data only. This paper proposes an improved solution based on wavelet transform and self-organized neural network. The measured voltage and current signals are preprocessed first and then decomposed using wavelet multiresolution analysis to obtain the high frequency details and low frequency approximations. The patterns formed based on high frequency signal components are arranged as inputs of neural network #1, whose task is to indicate whether the fault is internal or external. The patterns formed using low frequency approximations are arranged as inputs of neural network #2, whose task is to indicate the exact fault type. The new method uses both low and high frequency information of the fault signal 10 achieve an advanced line protection scheme. The proposed approach is verified using frequency-dependent transmission line model and the test results prove its enhanced performance. A discussion of the application issues for the proposed approach is provided at the end where the generality of the proposed approach and guidance for future study are pointed out. [ABSTRACT FROM AUTHOR]

This paper proposes a condition classification system using wavelet transform, feature evaluation and artificial neural networks to detect faulty products on the production line of reciprocating compressors for refrigerators. The stationary features of vibration signals are extracted from statistical cumulants of the discrete wavelet coefficients and root mean square values of band-pass frequencies. The neural networks are trained by the sample data, including healthy or faulty compressors. Based on training, the proposed system can be used on the automatic mass production line to classify product quality instead of people inspection. The validity of this system is demonstrated by the on-site test at LG Electronics, Inc. for reciprocating compressors. According to different products, this system after some modification may be useful to increase productivity in different types of production lines. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results. [ABSTRACT FROM AUTHOR]

Fault classification based upon vibration measurements is an essential building block of a conditional based health usage monitoring system. Multiple sensors are incorporated to assure the redundancy and to achieve the desired reliability and accuracy. The shortcoming of using multiple sensors is the need to deal with a high dimensional feature set, a computationally expensive task in classification. It is vital to reduce the feature dimension via an effective feature extraction and feature selection algorithm. A simple wavelet based feature selection scheme is proposed herein, uniquely built by local discriminant bases and genetic optimization. This scheme overcomes the disadvantages faced by the existing feature selection methods by producing a generic feature set, reducing the dimensionality of features, and requiring no prior information of the problem domain. The proposed feature selection scheme is based upon the strategy of "divide and conquer" that significantly reduce the computation time without compromising the classification performance. The simulation results show the proposed feature selection scheme provides at least 65% reduction of the total number of features at no cost of the classification accuracy. [ABSTRACT FROM AUTHOR]

Two methods for classification of transmission line faults are presented and compared, one based on a wavelet analysis and the other on an artificial neural network (ANN). While the wavelet analysis based approach requires consideration of wavelet multiresolution analysis (MRA) level-1 details of the three phase currents and delta currents, the ANN based approach requires the samples of three phase currents as inputs. Simulation studies using EMTP and MATLAB and considering wide variations in fault location, fault inception angle, and fault point resistance for different types of faults have shown that the wavelet analysis based method has an edge over the ANN based method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fault diagnosis method for a group of distribution transformers using the graphical information of frequency responses. For a group of transformers with similar capacities and ratings, the patterns of transformer's magnitude plots appear to be almost identical with minor variations of amplitudes and locations of existing peaks and valleys in the plots. Therefore, it is reasonable to establish prototypes composed of several peaks and valleys for magnitude plots and to investigate their changes for the purpose of diagnosis. We select six most significant regions for check regions, and their movements are encoded into a ternary string which is then compared with strings already constructed for various types of faults. Each string-by-string comparison is quantified into correlation values by computing Hamming distances and weighting selected bits. The fault type whose correlation values is largest is determined as a most probable fault in a test transformer. The diagnosis performance of the proposed method is proven to be reliable via experiment data on several types of faults. [ABSTRACT FROM AUTHOR]

The great development in computing power has allowed the implementation of artificial neural networks (ANNs) in the most diverse fields of technology. This paper shows how diverse ANN structures can be applied to the processes of fault classification and fault location in overhead two-terminal transmission lines, with single and double circuit. The existence of a large group of valid ANN structures guarantees the applicability of ANNs in the fault classification and location processes. The selection of the best ANN structures for each process has been carried out by means of a software tool called SARENEUR. [ABSTRACT FROM AUTHOR]

This paper presents an integrated structure for a passive and active fault tolerant control (FTC) design approach in the framework of a robust nonlinear control technique called Dynamic Surface Control (DSC). As motivated by the automated vehicle application, we consider two categories of possible faults: pre-specified ( a priori ) and non-specified faults. It is first shown that DSC can be considered as a passive FTC approach in the sense that it gives simultaneous robust stability to a set of nonlinear systems even in the presence of model uncertainties and the pre-specified faults. Then, the non-specified fault is classified depending on the fault’s impact on the closed-loop system and isolatability from a fault detection and diagnosis (FDD) system. If a fault is both intolerable and isolatable, an active FTC approach is taken which includes FDD and controller reconfiguration. More specifically, trajectory reconfiguration is considered to accommodate the actuator fault, i.e., to compensate for the performance loss due to the fault within the framework of a switched hierarchical structure. Finally, the integrated structure for the longitudinal control of an automated transit bus is designed through the proposed method. Simulation results of the fault tolerant controller are shown for both single and multiple multiplicative faults. This controller was implemented on the California PATH transit buses in a demonstration of automated public transportation technology in San Diego, California in August of 2003. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy-logic-based algorithm to identify the type of faults for digital distance protection system has been developed. The proposed technique is able to accurately identify the phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault resistances, inception angle, and loading levels. The proposed method needs only three line-current measurements available at the relay location and can perform the fault classification task in about a half-cycle period. Thus, the proposed technique is well suited for implementation in a digital distance protection scheme. [ABSTRACT FROM PUBLISHER]

Abstract: Present nomenclature of faults and flanking structures is ambiguous. This paper presents a system for description of flanking structures, based on geometric parameters and independent of kinematic frame. The description can be made using two levels of accuracy. A qualitative method is described using four geometric features: tilt, slip, lift and roll. This method is suggested for practical use in the field, since it does not involve measurements or complicated procedures. In parallel, a quantitative approach is also presented, based on analytical modelling of Bézier curves. This method requires measurement of geometric features and involves mathematical treatment, but allows comparison between different flanking structures. [Copyright &y& Elsevier]

Abstract: This paper presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line fed from both ends are used. The peak absolute value, the mean of the peak absolute values and summation of the 3rd level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are also examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 230kV, 200km long line fed from both ends and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also. [Copyright &y& Elsevier]

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this papa, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution. [ABSTRACT FROM AUTHOR]

Discrete wavelet transform is used to analyze faulty signals for a transmission system employing a thyristor controlled series capacitor at the midpoint of a line. Different frequency components of current signals are considered to select the phase(s) involved with a fault. To identify the fault section the new approach utilizes the difference in high frequency components of the current signals. For both phase selection and section identification tasks new energy and standard deviation-based indices are also calculated for the transmission system. [Copyright &y& Elsevier]

Series capacitor protected by metal-oxide varistor and air-gap arrangement imposes problems to line protection and other online decisions. Discrete wavelet transform integrated with a fuzzy logic system is designed for fault classification of a transmission line possessing a series capacitor at the midpoint. The approach uses information obtained from the wavelet decomposition of current signals for faulty phase selection and section identification. Two different FLSs are designed for the two classification objectives in this paper. [ABSTRACT FROM AUTHOR]

This article presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line are used. The peak absolute value, the mean of the peak absolute values, and summation of the third level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 400 kV, 300 km long line and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also. [ABSTRACT FROM AUTHOR]

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs.Because of the randomness and asymmetric characteristics of HIFs, the modelling of HIF is difficult and many papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients programme.This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square values of three-phase currents and zero sequence current.The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle. [Copyright &y& Elsevier]