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Purpose: The global interest of developing monitoring system is increasing due to the continuous challenges in reliability and accuracy. Automatic fault detection and diagnosis of rotating machinery play an important role for the high efficiency and reliability of modern industrial systems. The key point of having high accurate automatic model for fault detection and diagnosis is obtaining defect features and choosing a representative approach for the model. Methods: In this paper, a model is developed based on Mel Frequency Cepstral Coefficients (MFCC) and gammatone cepstral coefficients (GTCC) that are computed for the input signal frames. Additionally, two global representations (feature concatenation and feature statistics) are adopted to feed Support Vector Machine (SVM) and a temporal representation is used with Long Short-Term Memory (LSTM) and Echo State Network (ESN) classification models. To generalize the proposed model, the experiments are evaluated based on two different datasets (PHM09 and DDS), where the PHM09 contains samples of helical and spur gears while the DDS contains samples from parallel and plenary gearboxes. Results: The results show that the proposed SVM model based on feature concatenation can effectively detect faults from gears and outperforms the other existing methods in the state-of-the-art studies. Conclusion: Base on the result of this paper, a global representation by concatenating frame-based features outperforms global statistical and time-series feature representations. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of fault detection and categorization methods in TCSC (Thyristor Controlled Series Capacitor)-compensated power systems. The analysis explores different techniques for extracting fault features from current data obtained from TCSC-compensated transmission lines. Machine learning algorithms, namely k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) are employed to categorize the extracted features, enabling fault detection and identification. To evaluate the efficacy of the proposed method, simulation tests were conducted on a test system, considering various fault scenarios. The simulations accounted for the presence of noise and TCSC correction, reflecting real-world operating conditions. The results demonstrate the efficiency of multiple approaches in accurately and swiftly detecting and classifying faults. By leveraging the capabilities of machine learning algorithms and extracting relevant features from current data, the proposed method offers a promising solution for fault detection and categorization in TCSC-compensated power systems. The ability to operate effectively even in the presence of noise and TCSC correction highlights the robustness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

The occurrence of multiple faults is a practical problem in the bearings of rotating machines, and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0. The present work investigated various combinations of bearing faults, including dual and multiple fault conditions. Two prevalent fault diagnosis methods were employed: vibration monitoring using time-frequency scalograms extracted through Continuous Wavelet Transform (CWT) and a non-invasive Infrared Thermography (IRT). A 2-D Convolutional Neural Network (CNN) was used to classify various combinations of fault conditions through automated feature extraction. The proposed methodology was validated at two constant speed conditions of 19 Hz and 29 Hz and continuously accelerated and decelerated speed conditions in the range of 19 Hz - 29 Hz. Adequate accuracy was achieved in both dual and multiple fault conditions in the case of vibration-based fault diagnosis, with a range of 99.39 % to 99.97 %. Meanwhile, in the case of proposed IRT-based fault diagnosis, 100 % classification accuracy was achieved for dual and multiple faults in all conditions. These results signify the robustness and reliability of the proposed methodology for dual and multiple fault diagnosis in bearings at constant and varying speed conditions. [ABSTRACT FROM AUTHOR]

In this research a new approach is introduced for detecting and classifying faults in a Thyristor-Controlled Series Capacitor (TCSC) compensated power system. Our proposed scheme utilizes both the Fast Walsh Hadamard Transform (FWHT) and machine learning algorithms. The FWHT is employed to extract fault features from current data obtained from the TCSC compensated transmission line, while machine learning algorithms such as K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are used to classify the extracted features for the purpose of fault detection and identification. To evaluate the performance of our proposed scheme, simulation studies were conducted on a test system under various fault conditions. The simulation results demonstrate that our approach is highly effective in accurately and quickly detecting and classifying faults, even when noise and TCSC compensation are present. This scheme has the potential to enhance the reliability and efficiency of power transmission systems. [ABSTRACT FROM AUTHOR]

A wind turbine (WT) is an electromechanical system that often operates under a wide range of production conditions. These electrical systems are nowadays expanding rapidly, and they have considerable importance due to their efficiency as renewable energy sources. This led to proposing an innovative and efficient solution with intelligent systems to maintain and ensure the safe and stable operation of these dynamic systems. Maintenance tasks are based on the development of high-performance diagnostic tools, which consist in detecting and locating correctly and upstream the various failures affecting this wind machine. Where, the condition monitoring and supervision systems must rely on reliable fault diagnosis techniques in order to: avoid breakdowns and unscheduled shutdowns, improve their operation, and increase their energetic performances. In order to ensure adequate maintenance actions for the wind system, the purpose of this article is to propose and develop a robust and intelligent fault diagnosis structure. In this work, Kalman filters (KF) as state estimators are used to observe the output states of the sub-systems in order to generate the appropriate residuals evaluated by predetermined thresholds. Adaptive and hybrid network-based fuzzy inference systems (ANFIS) have been employed for the evaluation and classification stages of the detected faults to minimize the degradation of the wind turbine. All possible faults of wind turbine systems, sensors, and actuators are tested and investigated in all parts: pitch angle systems, drive, and generator with converter. The developed fault detection and identification structure are tested on a horizontal WT benchmark model using different scenarios and faults. The simulation results show the ability of the proposed and developed diagnostic methodology to detect the faults occurring efficiently and correctly in the machine. Thus, by using this robust diagnostic strategy, the condition monitoring system can maintain and ensure stable and safe operation to generate sufficient electrical power. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, a novel technique for identifying and categorizing flaws in small-scale photovoltaic systems is presented. First, a supervised machine learning (neural network) was developed for the fault detection process based on the estimated output power. Second, an extra tree supervised algorithm was used for extracting important features from a current-voltage (I–V) curve. Third, a multi-stacking-based ensemble learning algorithm was developed to effectively classify faults in solar panels. In this work, single faults and multiple faults are investigated. The benefit of the stacking strategy is that it can combine the strengths of several machine learning-based algorithms that are known to deliver good results on classification tasks, producing results that are more precise and efficient than those produced by a single algorithm. The approach was tested using an experimental dataset and the findings show that it could accurately diagnose faults (a detection rate of around 98.56% and a classification rate of around 96.21%). A comparison study with different ensemble learning algorithms (AdaBoost, CatBoost, and XGBoost) was conducted to evaluate the effectiveness of the suggested method. [ABSTRACT FROM AUTHOR]

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today's machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time savings and high success rates. Deep transfer learning models also improve diagnosis accuracy under certain conditions by greatly reducing human intervention. Diagnosis at the right time is very important for the sustainability and efficiency of industrial production. A technique based on continuous wavelet transform (CWT) and two dimensional (2D) convolutional neural networks (CNN) is presented in this paper to detect fault size from vibration data of various bearing failure types. Time-frequency (TF) color scalogram images for bearing vibration signals were obtained using the CWT method. Using AlexNet, GoogleNet, Resnet, VGG16, and VGG19 deep transfer learning methods with scalogram images, fault size prediction from vibration signals was performed. Five different transfer deep learning models were used for three different data sets. It was observed that the success rates obtained varied between 96.67% and 100%. [ABSTRACT FROM AUTHOR]

The main objective of this article is to develop an enhanced ensemble learning (EL) based intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems. The developed EL based techniques consist in combining multiple learning models instead of using a single learning model. To do that, three EL-based FDD techniques are proposed. First, an EL technique that merges the benefits of Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) is presented. The developed method contributes to the reduction of the overall diagnosis error and has the ability to combine various models. However, classical EL models ignore the time-dependence of PV measurements. In addition, the PV system data are frequently time-correlated. Therefore, kernel PCA (KPCA)-based EL and reduced KPCA (RKPCA)-based EL techniques are developed to take into consideration the dynamic and multivariate natures of the PV measurements. The two proposed KPCA -based EL and RKPCA-based EL techniques are addressed so that the features extraction and selection phases are performed using the KPCA and RKPCA models and the sensitive and significant characteristics are transmitted to the EL model for classification purposes. The presented results prove that the proposed EL based methods offer enhanced diagnosis performances when applied to PV systems.

The photovoltaic (PV) array is the most sensible element in PV plants, which is subject to different type of faults and defects. Thus, to keep these plants working efficiently they should be monitored and protected carefully. Some faults if they are not detected and isolated promptly they may lead to hazardous risks. The diagnosis of PV systems is widely addressed and recently machine learning (ML) and deep leaning (DL) methods drawn the attention of many researchers. Most applications of ML methods are based on the use of the I–V curves measurement, as enough information and features can be extracted from the curves, to detect and classify faults. These methods showed their capability to classify some faults, like line to line, degradation, disconnected PV modules, partial shading effect, and bypass diode faults. Another approach is based on the use of thermal or electroluminescence images of PV modules/arrays to detect and identify defects, such as hot spot, snails crack, and others. In this paper, different ML and ensemble learning (EL) methods are evaluated for fault diagnosis of PV arrays. The focus is mainly on the detection and classification of some complex faults that may affect the PV arrays, i.e., multiple faults, and faults with similar I–V curves, that are not evaluated before. The results showed the ability of the methods developed to detect faults with very good accuracy (classification rate = number of classified instances/total instances), within 99%, while the classification faults is done with an acceptable accuracy, within 81.73%. Through this study it is shown when really ML and EL methods should be used, and some recommendations, challenges and future directions in this topic are presented. [ABSTRACT FROM AUTHOR]

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In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertaintie rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively. [ABSTRACT FROM AUTHOR]

Nonlinear characteristics of solar cells and changes in environmental conditions, such as temperature, and in particular, the intensity of daytime irradiation, make it difficult to identify faults by the conventional means of protection. Therefore, a variety of machine learning techniques are proposed for fault detection in photovoltaic (PV) arrays. In this regard, classifying and identifying the location of a fault event is essential. In addition to fault recognition, selecting the method of fault correction is another issue to be addressed. However, there are scarce investigations in this field. In this paper, a comprehensive method for identifying, classifying, locating, and correcting faults is introduced. The proposed method is assessed with the expansion of the diagnostic space of the graph-based semisupervised learning algorithm and an increased number of class labels. After identifying the type and location of a fault, the system temporarily isolates the fault to function without interruption until it is fully corrected. The problem of overlapping cell data in normal and fault-prone modes is resolved by applying different methods of normalization. The results show that all faults including unlearned and learned in a wide range of environmental conditions, where possible PV arrays are experienced, are properly identified and corrected. Moreover, our studies demonstrate that the proposed system mitigates the output voltage variations over a fault-prone mode. [ABSTRACT FROM AUTHOR]

In this study, a novel intelligent relaying scheme is proposed for fault classification and faulted phase selection using magnitude of differential power (MODP). The DP is the multiplication of difference between the magnitude of the real and estimated voltage and current phasors. The MODP for each phase is used as an input data to the data mining model known as decision tree (DT). The DT is used to generate thresholds to detect and classify the faults, since determination of robust thresholds is an important challenge in protective relay engineering. The proposed method has been tested for different operating modes of single‐circuit transmission line in Simulink/MATLAB software. Simulation results indicate that the proposed method is able to detect and classify the faults with acceptable accuracy, in less than half cycle. [ABSTRACT FROM AUTHOR]

Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, turbines, etc.) that have been identified as one of the primary causes of failure in these machines. This makes bearing fault diagnosis (detection, classification, and prognosis) an economic very relevant topic, as well as a technically challenging one as evaluated by the extensive research literature on the subject. This paper employs a systematic methodology to identify, summarize, analyze, and interpret the primary literature on fuzzy formalisms for bearing fault diagnosis from 2000 to 2017 (March). The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis (summary, classification, and critique) of the available approaches resorting to fuzzy formalisms in this trendy topic. A discussion on a new promising future research direction is provided. A comprehensive list of references is also included. [ABSTRACT FROM AUTHOR]

In this paper, a supervisory algorithm for attitude determination sensors of a three-axis satellite is suggested to classify different fault types of this system. The presented solution is based on the computation of rotation matrices between the orbital and body frames. Using the Euler angles provided by these rotations, some variance measures are computed, which utilized as an analytical tool for the determination of faulty sensors and the related fault sources. According to the mentioned idea, it is shown that the fault sources could be categorized into four levels. Each level shows the fault extent and its criticality. To include the reliability aspects in this paper, a failure mode effect criticality analysis is conducted to determine different failure modes, which really occur in sensors. For this, the probability levels and the severity numbers were assigned to derive the critical ones. It is shown that most of the critical failure modes could be isolated by the proposed methods; in other words, the design process is based on real faulty scenarios that can improve the system reliability. Finally, by performing some numerical simulations, performance of the developed algorithms are validated. [ABSTRACT FROM AUTHOR]

Highlights • Shaded PV cells degrade the performance of PV systems. • I-V curves contain useful information for health monitoring. • Data driven method for fault diagnosis can deal with non-controlled solar irradiance. • Principal component analysis has proven its efficacy under real working conditions. Abstract Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required. [ABSTRACT FROM AUTHOR]

The ever increasing power demand and stress on reducing carbon footprint have paved the way forwidespread use of photovoltaic (PV) integrated microgrid. However, thedevelopment of a reliable protection scheme for PV integrated microgrid ischallenging because of the similar voltage‐current profile of PV array faultsand symmetrical line faults. Conventional protection schemes based onpre‐defined threshold setting are not able to distinguish between PV array andsymmetrical faults, and hence fail to provide separate controlling actions forthe two cases. In this regard, a protection scheme based on sparse autoencoder(SAE) and deep neural network has been proposed to discriminate between arrayfaults and symmetrical line faults in addition to perform mode detection, faultdetection, classification and section identification. The voltage‐currentsignals retrieved from relaying buses are converted into grey‐scale images andfurther fed as input to the SAE to perform unsupervised feature learning. Theperformance of the proposed scheme has been evaluated through reliabilityanalysis and compared with artificial neural network, support vector machine anddecision tree based techniques under both islanding and grid‐connected mode ofthe microgrid. The scheme has been also validated for field applications byperforming real‐time simulations on OPAL‐RT digital simulator. [ABSTRACT FROM AUTHOR]

This study deals with the faults' detection and classification in AC transmission lines based on power spectral density (PSD), introducing PSD in time and frequency for analysing transient information under faulted conditions. The discrete wavelet transform is used for scaling current signals in time–frequency at different decomposition levels by approximation and detail coefficients. Then, a wavelet‐covariance matrix is shaped with the aim to obtain its PSD, being this the key to detect and classifying faults. Results show that the proposed method attains the detection in a short time and the classification is accomplished via the Hellinger distance, whose straightforward implementation is carried out in this study. The classification process is compared adopting different classifiers to cope with a set of signals in a time frame. Finally, the proposal is extensively assessed using real and simulated signals stemming from multiple fault cases of radial and interconnected power grids. [ABSTRACT FROM AUTHOR]

Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for detecting and classifying faults in transmission lines is proposed. The algorithm is based on mathematical morphology and initial current travelling waves. A new morphological edge detection (MED) filter to extract the transient features from the original fault signal is designed. This MED filter can fast and accurately detect the arrival time and polarity of travelling waves in all conditions. The appropriate criteria of fault classification and faulted‐phase selection are introduced based on polarity of initial current travelling waves. The simulations based on the electromagnetic transients program and MATLAB have been done to evaluate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

This paper presents a complete protection scheme for detecting, classifying and locating the fault in HVDC transmission lines using support vector machines (SVM). SVM has been used for protective relaying application in HVAC transmission line, however very limited works have been reported for HVDC transmission line. In this work, a ±500 kV HVDC transmission system is developed in PSCAD/EMTDC and the measurement signals obtained are analyzed in MATLAB. The rectifier side AC RMS voltage, DC voltage and current on both the poles are continuously monitored, and given as input to the SVM binary classifier in order to detect the presence of fault in the line. Once a fault is detected, the SVM multi‐class classification module predicts the type of fault and the SVM regression algorithm predicts the location of fault. The feature vector used in the classification and location modules is the standard deviation of the signals over half cycle before and after the occurrence of fault. The method proposed is simple as it requires single‐end data and a direct standard deviation of one cycle data gives very accurate results. The detection and classification modules are 100% accurate whereas the fault location module has a mean error of 0.03%. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for detecting and classifying faults in high‐voltage transmission lines is proposed. The algorithm is based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The DWT is used for extracting the currents' high‐frequency components under fault conditions. Signals under each fault condition are scaled in frequency, in order to build a wavelet matrix. By means of the SVD, the maximum singular value is calculated and employed in this proposal. The attained results exhibit that the maximum singular value represents a good indicator for the issue. This novel approach for detecting and classifying faults in power systems is called maximum wavelet singular value. Phase‐to‐ground, two‐phase to ground, and three‐phase faults' simulations under different fault impedances are carried out by DIgSILENT Power Factory. The analysed fault conditions are evaluated demonstrating that the proposal reduces the computational burden and the time detection. [ABSTRACT FROM AUTHOR]

The proposed work develops a decision tree-induced fuzzy rule base intelligent protection scheme for fault detection and classification in a microgrid with multiple distributed generation interfaces. The proposed protection scheme retrieves one cycle post-fault current signal samples of each phase from fault inception at bus ends of the respective feeder to derive some differential features. The retrieved current samples are pre-processed using S-transform to obtain a time–frequency contour. The statistical features, such as energy, mean, standard deviation, and entropy, are computed from the time–frequency contour, which is further used to calculate the differential features. The differential features are used to build the fault classification tree. From the decision tree classification boundaries, the fuzzy membership functions are drawn, and further, the corresponding fuzzy rule base is generated for the final relaying decision. The proposed scheme is developed on a MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA) platform, including wide variations in faulted conditions, and the extensive test results indicate that the proposed intelligent relaying scheme can reliably provide protection measures for microgrids with different modes of operation. [ABSTRACT FROM PUBLISHER]

Diagnosis aims at predicting the health status of components and systems. In photovoltaic systems, it is vital to guarantee energy production and extend the useful life of photovoltaic power plants. Multiple prediction and classification algorithms have been proposed for this purpose in the literature. The accuracy of these algorithms depends directly on the quality of the data and the features with which they are tuned or trained. In this paper, an innovative approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage. This approach first discriminates severely affected photovoltaic panels using basic electrical features. In a second step, it discriminates the other faulty panels using more elaborated time–frequency features and selecting the most relevant features through correlation and variance analysis. Finally, the approach predicts the health status of photovoltaic panels using a nonlinear regression method named partial least squares. This later is then combined with linear discriminant analysis and compared. The approach is validated with real current data from a photovoltaic plant composed of twelve photovoltaic panels with power between 205 and 240 Wp in three health states, namely broken glass, healthy, and big snail trails. The results obtained show that the proposed approach efficiently predicts the three health states. It determines the level of degradation of the panels, which indicates priorities to corrective and predictive maintenance actions. Furthermore, it is cost-effective since it uses only electrical measurements that are already available in standard photovoltaic data acquisition systems. Above all, the approach is generic and it can be easily extrapolated to other diagnosis problems in other domains. [ABSTRACT FROM AUTHOR]

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half‐cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal‐operation so it is reliable, accurate and secure. [ABSTRACT FROM AUTHOR]

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]

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]

The rapid development of photovoltaic (PV) technology and the growing number and size of PV power plants require increasingly efficient and intelligent health monitoring strategies to ensure reliable operation and high energy availability. Among the various techniques, Artificial Neural Network (ANN) has exhibited the functional capacity to perform the identification and classification of PV faults. In the present review, a systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted. For each application, the targeted PV faults, the detectable faults, the type and amount of data used, the model configuration and the FDD performance are extracted, and analyzed. The main trends, challenges and prospects for the application of ANN for PV FDD are extracted and presented. • Applications of ANN techniques and hybrid application for PV FDD are summarized. • Detectable PV faults with ANN technique and diagnosis performance are investigated. • Reported public PV database of 1D data and 2D images are summarized. • Common trends for the selection of model for different PV faults are identified. • Challenges and prospects for PV FDD using ANN techniques are presented. [ABSTRACT FROM AUTHOR]

In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges' currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection. The two-terminal MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN, the SoftMax classifier performed better in detection and classification accuracy as well as testing speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based DNN and SoftMax classifier. [ABSTRACT FROM AUTHOR]

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines. [ABSTRACT FROM AUTHOR]