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Distribution system voltage profile management at each bus and fault detection and classification are often challenged by complex and changing network configurations. The distribution system voltage profile improvement issue is addressed by placing distributed generation (DG) units at different locations in the network. By placing the DG units at appropriate places in IEEE 33 bus radial distribution networks by a proposed reinforcement learning (RL) algorithm, the voltage profile of each node is improved and power loss in the network is minimized. There is a 69% reduction in active power losses compared to losses without DG. Furthermore, an innovative method for fault detection and classification is developed that uses a convolutional neural network (CNN) cascaded with a long short-term memory network (LSTM) and attention mechanisms (AMs). To extract dynamic information from the data, phasor measurement units (PMUs) placed on different buses are used as input for the CNN architecture. AM strengthens important information. A mapping weight and parameter learning approach allows AM to assign different weights to concentrate on LSTM characteristics and improve learning accuracy. Low and high impedance faults are tested as well as various non-faulty events. The scheme's performance is compared with that of other deep learning techniques through reliability analysis, and the time taken for fault detection (FD) is also determined. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques. [ABSTRACT FROM AUTHOR]

Summary Node faults are often influenced by factors such as physical component failure, communication module errors, battery exhaustion, and environmental factors. Various researchers have contributed to addressing the problem of detecting, diagnosing, and classifying faults in sensor networks. However, computational complexity and fault occurrence probability increase with the size of the network. In this context, an enhanced optimized layered diagnosis framework (OLDF) is proposed for detecting, diagnosing, and classifying sensor node faults. OLDF utilizes a deep belief network (DBN) with chaos quantum‐behaved particle swarm optimization (CQ‐PSO) for efficient diagnosis and classification of various sensor faults in the network. The proposed OLDF consists of two layers. In the first layer, optimized DBN with CQ‐PSO algorithm is employed to determine the fault type of the sensor nodes. Then, the fault type of sensor nodes that are classified in the first layer is given as input into the second layer of the OLDF algorithm to determine the fault severity in the network. Experimentation has been carried out by using the NS3 simulator for the OLDF framework with performance metrics such as classification accuracy, diagnosis accuracy, false positives, fault alarm rate, and average energy consumption. The validity of the proposed framework has been verified through comparisons with existing works such as MFD, INSA, FDRFC, and HFD. From the comparative analysis carried out, it is apparent that the proposed framework achieves a high classification accuracy of 89.94% and diagnosis accuracy of 93.21%, with less false positive rate, and utilizes minimum energy when compared with its counterparts. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of failures in low-voltage terminal connections within power transformers and proposes a fault classification methodology based on infrared thermography (IRT). Low-voltage terminal connections play a critical role in the reliable operation of power transformers, and their failures can lead to severe operational issues. In this study, we employ IRT as a noninvasive and efficient diagnostic tool to identify and classify various types of failures, including loose connections, overheating, and corrosion. The research involves the collection of infrared thermograms (IRT images) from the low-voltage terminals of power transformers under different operating conditions. The proposed methodology demonstrates its effectiveness in detecting and classifying low-voltage terminal connection failures, thereby enabling timely preventive maintenance and minimizing the risk of transformer malfunctions. This research contributes to enhancing the reliability and longevity of power transformers, reducing downtime, and optimizing maintenance practices in the power industry. [ABSTRACT FROM AUTHOR]

AbstractHigh-dimensional data streams are ubiquitous in modern manufacturing, due to their ability to provide valuable information about the industrial system’s performance on a real-time basis. If a shift occurs in a production process, fault diagnosis based on the data streams is of critical importance for identifying the root cause. Existing methods have largely focused on controlling the total missed discovery rate without distinguishing missed signals for positive versus negative components of the shift vector. In practice, however, losses incurred from the two directional shifts can differ substantially, so it is desirable to constrain the proportions of missed signals for positive and negative components at two distinctive levels. In this article, we propose a fault classification procedure that controls the two proportions separately. By formulating the problem as Lagrangian multiplier optimization, we show that the proposed procedure is optimal in the sense that it minimizes the expected number of false discoveries. We also suggest an iterative adjustment algorithm that converges to the optimal Lagrangian parameters. The asymptotic optimality for the data-driven version of our procedure is also established. Theoretical justification and numerical comparison with state-of-the-art methods show that the proposed procedure works well in applications. [ABSTRACT FROM AUTHOR]

PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach. [ABSTRACT FROM AUTHOR]

To address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time–frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA‐KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

AbstractOne important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Finally, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods. [ABSTRACT FROM AUTHOR]

A fault in a power system is an anomalous state that must be recognized as soon as feasible. To minimize the repercussions of the fault, such as damages occurred to the device, loss of tangible assets and loss of human resources, it is critical to notice the problem promptly. In a power distribution system, there are several approaches for detecting different types of faults. In this paper, a neoteric approach using Bayesian optimized Convolutional Neural Network is used to detect and classify different symmetrical as well as unsymmetrical faults in power distribution systems. The effectiveness of the proposed CNN model is validated for an IEEE 13 bus radial distribution system grid modeled (and simulated) in PSCAD. Time series of the measured 3-phase fault currents (for eleven different categories of faults) are used to create training & testing data. This data has been imported in MATLAB software to develop a CNN classifier (whose hyper-parameters are optimized by using a Bayesian optimizer) for faults in power distribution system, under distinct fault situations by varying fault resistance, faulty node and fault inception angles. Findings of simulation clearly indicate that proposed model has very high categorizing accuracy and is superior and competitive to other techniques available in literature. [ABSTRACT FROM AUTHOR]

This paper proposes a novel unsupervised classification method to solve the problem of mechanical fault diagnosis under the condition of unknown number of fault types. The proposed method combining the three data processing stage. First, the deep encoding neural networks is used to complete the abstract signal feature representation under the unsupervised conditions. Second, the feature dimensionality reduction technique based on manifold learning is used to complete the low-dimensional mapping of the feature space. Third, the spatial clustering based on density criterion is introduced to classify the different fault samples. This paper uses two fault signals dataset to complete the performance verification experiment. The experimental results show that the DMDUC method respectively achieves the classification accuracy of 99.7 % and 100 % on the two datasets. [ABSTRACT FROM AUTHOR]

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on. [ABSTRACT FROM AUTHOR]

Series compensation poses serious challenges to the protection schemes of transmission lines (TLs). Moreover, with the penetration of distributed generation (DG), the conventional protection schemes fail to detect the fault and identify the faulty phase as the backfeed currents from the DG units mask the faulty phase. This work presents an algorithm for the protection of series-compensated TLs (SCTL) integrated with wind farms. The algorithm uses the phase difference between the positive sequence currents at the two ends of the TL to discriminate internal and external faults. The faulty phase for internal fault is identified from the difference in complex power at the two ends of the TL. Extensive simulations with several fault and system parameters assess the reliability and applicability of this technique. [ABSTRACT FROM AUTHOR]

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from −6 to 6 dB signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μ PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μ PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μ PMU on a densely-noded distribution network. [ABSTRACT FROM AUTHOR]

During power supply swings, current and voltage waveforms are modulated with additional frequency elements which lead to maloperation of relays. This maloperation may cause cascade tripping of transmission lines, which results in the failure of electrical grid networks. Power Swing Blockers (PSBs) are employed to block and prevent maloperation of distance relays during power swing. Along with power swing, the presence of TCSC compensation in transmission line deteriorates the input signals to relays which make fault classification a complex task. During the PSB blocking period, modulated signals distorted with non-linearities of MOV operation complicate the fault diagnosis functioning. This challenge motivates to develop an algorithm for fault classification during power swings. To address aforementioned challenges, a Wigner Ville energy-based fault classification technique is proposed in this work. A modified 9 bus WSCC system with TCSC compensation is used for testing and validation of the proposed scheme. The scheme is tested for different cases like symmetrical, close-in fault, asymmetrical high resistance fault, and CT saturation, etc. The scheme is also validated against transient conditions like load and capacitor switching. The performance of proposed scheme is compared with existing schemes in literature. The above scheme is also implemented and validated on an Intel Cyclone V SOC FPGA. The results show that the proposed scheme classifies faults accurately during the simulation as well as on hardware platform. [ABSTRACT FROM AUTHOR]

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN. [ABSTRACT FROM AUTHOR]

The development of microgrids can prove to be a game changer in the realization of future smart grids. The increasing trends of automation in domestic loads and smart homes have increased the penetration of DC loads at the distribution level. Hence, the development of a low- or medium-voltage DC microgrid seems the need of the hour to match this changing scenario. Another point is that the various distributed generation (DG) sources like PV, batteries, fuel cells, etc., also provide energy in DC form. Hence, integrating these sources into the DC system also becomes convenient as the power conversion at each point of connection will no longer be needed. But, due to the lack of universally accepted standards and technological advancements, the implementation of DC microgrids is still limited to very small-scale usage. The unavailability of an efficient and reliable protection system is one such issue that is hindering the implementation of DC microgrids on a larger scale. Therefore, a thorough examination of the several DC microgrid protection difficulties and challenges is necessary. Thus the purpose of this article is to provide a comprehensive analysis of the protection challenges, and the currently available protection schemes for DC microgrids and to highlight the gaps for future research to enable the development of a more reliable and efficient protection system. [ABSTRACT FROM AUTHOR]

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail. [ABSTRACT FROM AUTHOR]

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μ PMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μ PMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μ PMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μ PMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have immense potential to generate clean energy, and their adoption has grown significantly in recent years. A PV fault is a condition of a PV module that is unable to produce optimal power due to environmental factors, such as shading, hot spots, cracks, and other defects. The occurrence of faults in PV systems can present safety risks, shorten system lifespans, and result in waste. Therefore, this paper discusses the importance of accurately classifying faults in PV systems to maintain optimal operating efficiency, thereby increasing the financial return. Previous studies in this area have largely relied on deep learning models, such as transfer learning, with high computational requirements, which are limited by their inability to handle complex image features and unbalanced datasets. The proposed lightweight coupled UdenseNet model shows significant improvements for PV fault classification compared to previous studies, achieving an accuracy of 99.39%, 96.65%, and 95.72% for 2-class, 11-class, and 12-class output, respectively, while also demonstrating greater efficiency in terms of parameter counts, which is particularly important for real-time analysis of large-scale solar farms. Furthermore, geometric transformation and generative adversarial networks (GAN) image augmentation techniques improved the model's performance on unbalanced datasets. [ABSTRACT FROM AUTHOR]

In order to maximise fuel cell reliability of operation and useful life span, an accurate online health assessment of the fuel cell system is essential. Existing algorithms for fault detection in fuel cell systems are based on sensing elements, control methods, and statistical/probabilistic models. In this paper, an artificial neural network (ANN) will be developed to detect and classify faults in proton-exchange membrane (PEM) fuel cell systems. As the ANN model developed within the PEM system relies on the input and output current and voltage, additional sensing devices are not required within the system. Based on an experimental setup using a 3-kW fuel cell system, it was found that the proposed model was able to detect faults associated with the reduction/increase of fuel pressure, H 2 consumption rate, and voltage regulation changes in the dc-dc converter with >90% accuracy. In the proposed model, historical data is required to train and validate the ANN algorithm, but after this is complete, no human intervention is required afterward. [Display omitted] • An ANN-based algorithm for identifying faults in PEM fuel cells. • Voltage regulation of dc-dc converters, fuel pressure, and hydrogen consumption rate can be detected. • A 3-kW fuel cell system was used to demonstrate the effectiveness of the ANN model. • The detection accuracy rate is always over 90% for all seven types of faults considered. • Performance of the ANN algorithm is compared with that of classical methods. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach to estimate the failures of various components in an induction motor using motor current signature analysis. Conventional sensor-based fault detection methods lead to huge manpower and require greater number of sensors. To overcome these drawbacks, current signature base fault detection is proposed. An advanced spectral analysis, namely discrete wavelet transform (DWT), is used for frequency domain analysis. This paper also presents fault severity estimation using feature extraction-based evaluation of DWT coefficients. As the DWT gives many coefficients at higher level decomposition which is essential for high resolution, fault classification and severity index become challenging. To address this issue, artificial neural network (ANN) algorithm is used after DWT decomposition. The fault severity is predicted by proposed fault indexing parameter of various features like energy, standard deviation, skewness, variance, RMS values. Conventional algorithms like support vector machine, k‐nearest neighbour, local mean decomposition-singular value decomposition and extreme learning machine have given maximum of 98–99% accuracy, Whereas the proposed DWT-based ANN has given 100% accuracy with tanh function. Moreover, the testing loss with this function is also very less. Experimental results have affirmed the accuracy of proposed fault detection of various faults in induction motor of rating 3—Phase, 1. 5KW, 440 V and 50 Hz. [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]

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]

Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid's distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques. [ABSTRACT FROM AUTHOR]

The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately. [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]

Modern societies need a constant and stable electrical supply. After relying primarily on formal mathematical modeling from operations research, control theory, and numerical analysis, power systems analysis has changed its attention toward AI prediction/forecasting tools. AI techniques have helped fix power system issues in generation, transmission, distribution, scheduling and forecasting, etc. These strategies may assist today's large power systems which have added more interconnections to meet growing load demands. They make it simple for them to do difficult duties. Identification of problems and problem management have always necessitated the use of labor. These operations are made more sophisticated and data-intensive due to the variety and growth of the networks involved. In light of all of this, the automation of network administration is absolutely necessary. AI has the potential to improve the problem-solving and deductive reasoning approaches used in fault management. This study implements a variety of artificial intelligence and deep learning approaches in order to foresee and predict the corrective measures that will be conducted in response to faults that occur inside the power distribution network of the Grid station in Tabuk city with regard to users. The Tabuk grid station is the source of the data that was gathered for this purpose; it includes a list of defects categorization, actions and remedies that were implemented to overcome these faults, as well as the number of regular and VIP users from 2017 to 2022. Deep learning, the most advanced method of learning used by artificial intelligence, is continuing to make significant strides in a variety of domain areas, including prediction. This study found that the main predictors of remedial measures against the fault occurring in the power systems are the number of customers affected and the actual cause of the fault. Consequently, the deep learning regression model, i.e., Gated Recurrent Unit (GRU), achieved the best performance among the three, which yielded an accuracy of 92.13%, mean absolute error (MAE) loss of 0.37%, and root mean square error (RMSE) loss of 0.39% while the simple RNN model's performance is not up to the mark with an accuracy of 89.21%, mean absolute error (MAE) loss of 0.45% and root mean square error (RMSE) loss of 0.34%. Significance of the research is to provide the maximum benefit to the customers and the company by using different AI techniques. [ABSTRACT FROM AUTHOR]

Bearings are critical components of motors. However, they can cause several issues. Proper and timely detection of faults in the bearings can play a decisive role in reducing damage to the entire system, thereby reducing economic losses. In this study, a hybrid fuzzy V-structure fuzzy fault estimator was used for fault diagnosis and crack size identification in the bearing using vibration signals. The estimator was designed based on the combination of a fuzzy algorithm and a V-structure approach to reduce the oscillation and improve the unknown condition's estimation and prediction in using the V-structure method. The V-structure surface is developed by the proposed fuzzy algorithm, which reduces the vibrations and improves the stability. In addition, the parallel fuzzy method is used to improve the robustness and stability of the V-structure algorithm. For data modeling, the proposed combination of an external autoregression error, a Laguerre filter, and a support vector regression algorithm was employed. Finally, the support vector machine algorithm was used for data classification and crack size detection. The effectiveness of the proposed approach was evaluated by leveraging the vibration signals provided in the Case Western Reserve University bearing dataset. The dataset consists of four conditions: normal, ball failure, inner fault, and outer fault. The results showed that the average accuracy of fault classification and crack size identification using the hybrid fuzzy V-structure fuzzy fault estimation algorithm was 98.75% and 98%, respectively. [ABSTRACT FROM AUTHOR]

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the audio signal of fan drive chain components was complicated and fault identification was difficult to realize with a single feature extraction method, a fault diagnosis method for audio signals based on multi-scale feature extraction was proposed. Firstly, the collected audio files of fan drive chain components were used to convert to digital signals and preprocess the data. Secondly, multi-scale feature extraction was performed on the audio signal. Five major features were extracted from the three dimensions of time domain, frequency domain and cepstral domain to form a multi-dimensional composite feature matrix. Then, the features were analyzed and the dimension of the feature matrix was reduced. Finally, the support vector machine（SVM） classification predictor was used to perform supervised learning on the multi-dimensional composite feature matrix, and the particle swarm optimization（PSO）was used to optimize the SVM parameter selection process. Through multiple sets of comparative experiments, the performance of PSO-SVM classification predictor on audio signal pattern classification was verified. The experimental results indicate that the extracted multi-scale features can well represent the information of the audio signal, and have certain robustness. The SVM classification model optimized by PSO eliminates the blindness of parameter selection, which can achieve more than 98%accuracy rate of audio signal pattern recognition for fan drive chain components, and has good generalization. [ABSTRACT FROM AUTHOR]

In order to effectively improve the reliability and accuracy of rolling bearing fault diagnosis, in view of the advantages of refined composite multi-scale reverse fluctuation dispersion entropy（RCMRFDE） method in describing the complexity of nonlinear sequences and feature extraction, a comprehensive rolling bearing fault detection method combining RCMRFDE and extreme learning machine（ELM） was proposed（this method includes health detection and fault classification）.Firstly, according to advantage of the significant difference between the health and fault vibration signal complexity of rolling bearing, RCMRFDE was used to detect the health state of rolling bearing in advance and screen out the healthy bearing. Then, RCMRFDE was used to extract the fault features of the remaining fault bearings, and the fault type was intelligently recognized based on the extreme learning machine（ELM）.Finally, based on two kinds of published rolling bearing fault data, the RCMRFDE+ELM method was tested with other five fault diagnosis methods, and the obtained results were compared, to verify the detection accuracy, classification accuracy, efficiency and reliability of the new method. The research results show that the proposed method can accurately detect whether there is a fault in the rolling bearing, and the average recognition accuracy of the fault rolling bearing was 99.96% and 99.67% respectively, which were higher than other methods. It also provides a detection threshold setting scheme and a fault diagnosis idea for establishing the health monitoring model of rolling bearings. [ABSTRACT FROM AUTHOR]

This work proposes a systematic approach to detect and classify bearing faults using vibration signals under varying speeds. The proposed approach consists of several steps, such as segmentation of signal consisting of maximum fault relevant information and extraction of features less influenced by varying speeds, and develop a machine learning model for online classification of bearing faults. Bearing when operating under time-varying speeds, the most critical and challenging step, is the demodulation of non-stationary and nonlinear vibration signals exhibiting severe modulations. The empirical wavelet transformation (EWT) algorithm has been used to decompose the raw signal into multiple mode functions (MFs), thereby detecting faults. However, these MFs contaminated by vestigial noise, when processed, mislead the detection of incipient bearing faults, thereby reducing EWT performance. Hence, this study addresses this by proposing the selection of the most impulsive MF for varying speed by estimating instantaneous frequency, which lies near bearing characteristic defect frequencies, thereby eliminating the possibility of vestigial noise being processed. Further, ten entropies, root-mean-square, and kurtosis are computed from the selected MF for statistical analysis. The results of the proposed approach are compared with the ensemble empirical mode decomposition to highlight the capabilities. Statistically significant fault discriminating features are identified using the Kruskal–Wallis test. These identified features are subsequently utilized by the Random Forest classifier. Thus, it has resulted in higher accuracy in detecting and classifying the different faults trapped by severe modulations. [ABSTRACT FROM AUTHOR]

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%. [ABSTRACT FROM AUTHOR]

In condition monitoring, accurate fault identification is an essential task for designing a proper maintenance strategy. Misalignment is one of the main faults in rotary machinery, because 70% of the failure occurs due to misalignment. Conventionally, the diagnosis of misalignment is carried out through vibration measurements. Especially, the presence of strong 2x vibration peak is generally accepted. Both angular and parallel misalignment shows peak at 2x, therefore, distinguishing misalignment type by using vibration signals alone is a difficult activity. This paper discusses classification of misalignment i.e., angular and parallel by using a diagnostic medium such as the acoustic emission and the rotor vibration signal. Vibro-acoustic sensors are used to collect data from the misaligned rotor system at two different loading, three different speed and three defect severity conditions. Time domain features are extracted and graded according to their significance using t test (One-way ANOVA) technique. Extracted features are used to train different algorithms. The outcome obtained using support vector machine (SVM) is 100% accurate. Vibro-acoustic sensor data fusion technique is employed to classify various forms of misalignment under different operating conditions. This work also intended to explore using a small amount of training data using different algorithms. The proposed method outperforms fault classification using vibration signal and acoustic signal separately. [ABSTRACT FROM AUTHOR]

This work proposes a systematic approach to detect and classify bearing faults using vibration signals under varying speeds. The proposed approach consists of several steps, such as segmentation of signal consisting of maximum fault relevant information and extraction of features less influenced by varying speeds, and develop a machine learning model for online classification of bearing faults. Bearing when operating under time-varying speeds, the most critical and challenging step, is the demodulation of non-stationary and nonlinear vibration signals exhibiting severe modulations. The empirical wavelet transformation (EWT) algorithm has been used to decompose the raw signal into multiple mode functions (MFs), thereby detecting faults. However, these MFs contaminated by vestigial noise, when processed, mislead the detection of incipient bearing faults, thereby reducing EWT performance. Hence, this study addresses this by proposing the selection of the most impulsive MF for varying speed by estimating instantaneous frequency, which lies near bearing characteristic defect frequencies, thereby eliminating the possibility of vestigial noise being processed. Further, ten entropies, root-mean-square, and kurtosis are computed from the selected MF for statistical analysis. The results of the proposed approach are compared with the ensemble empirical mode decomposition to highlight the capabilities. Statistically significant fault discriminating features are identified using the Kruskal–Wallis test. These identified features are subsequently utilized by the Random Forest classifier. Thus, it has resulted in higher accuracy in detecting and classifying the different faults trapped by severe modulations. [ABSTRACT FROM AUTHOR]

Recently, the concept of the microgrid (MG) has been developed to assist the penetration of large numbers of distributed energy resources (DERs) into distribution networks. However, the integration of DERs in the form of MGs disturbs the operating codes of traditional distribution networks. Consequently, traditional protection strategies cannot be applied to MG against short-circuit faults. This paper presents a novel intelligent protection strategy (NIPS) for MGs based on empirical wavelet transform (EWT) and long short-term memory (LSTM) networks. In the proposed NIPS, firstly, the three-phase current signals measured by protective relays are decomposed into empirical modes (EMs). Then, various statistical features are extracted from the obtained EMs. Afterwards, the extracted features along with the three-phase current measurement are input to three different LSTM network to obtain exact fault type, phase, and location information. Finally, a trip signal based on the obtained fault information is generated to disconnect the faulty portion from the rest of the MG. The significant feature of the proposed NIPS is that it does not need adaptive relaying and communication networks. Moreover, it is independent of the operating scenario and hence fault current magnitude. To evaluate the efficacy of the proposed NIPS, exhaustive simulations are performed on an international electro-technical commission (IEC) MG. The simulation results confirm the efficiency of the proposed NIPs in terms of accuracy, dependability, and security. Moreover, comparisons with existing intelligent protection schemes validate that the proposed NIPS is highly accurate, secure, and dependable. [ABSTRACT FROM AUTHOR]

Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for the feature recognition and fault classification of rotating machinery. First, vibration signal data from the rotating machinery is transformed into time–frequency feature 2-D image data by a continuous wavelet transform and used for fault classification with the neural network method. The adaptive deep convolution neural network (ADCNN) is then combined with the generative adversarial networks (GANs) to improve the performance of the feature self-learning ability from input data. Compared with different fault diagnosis methods, the proposed method has better performance for image feature classification in rotating machinery. [ABSTRACT FROM AUTHOR]

Today high speed protection of power system elements is necessary and realizable. This paper introduces time-domain protection techniques for transmission lines using voltage and current signals in Cartesian coordinates. Three techniques are proposed using this approach for line protection. Direction of fault is obtained using change in angle between Cartesian planes of voltage and current. In the second application on unit protection scheme, internal and external faults in transmission lines are discriminated using dot product of the normals to both end current planes. Fault type identification is obtained using the angle between incremental changes in currents in Cartesian coordinates. The output of each protection function using the new concept is derived in a quarter-cycle. The performance of each of the proposed techniques is tested for load variation, high resistance fault, different fault types, power swing, fault inception angles and signal with noise. Further, the proposed techniques are evaluated using field data from a 400 kV double circuit line. [ABSTRACT FROM AUTHOR]

In the modern induction motor (IM) drive system, the fault-tolerant control (FTC) solution is becoming more and more popular. This approach significantly increases the security of the system. To choose the best control strategy, fault detection (FD) and fault classification (FC) methods are required. Current sensors (CS) are one of the measuring devices that can be damaged, which in the case of the drive system with IM precludes the correct operation of vector control structures. Due to the need to ensure current feedback and the operation of flux estimators, it is necessary to immediately compensate for the detected damage and classify its type. In the case of the IM drives, there are individual suggestions regarding methods of classifying the type of CS damage during drive operation. This article proposes the use of the classical multilayer perceptron (MLP) neural network to implement the CS neural fault classifier. The online work of this classifier was coordinated with the active FTC structure, which contained an algorithm for the detection and compensation of failure of one of the two CSs used in the rotor field-oriented control (DRFOC) structure. This article describes this structure and the method of designing the neural fault classifier (NN-FC). The operation of the NN-FC was verified by simulation tests of the drive system with an integrated FTC strategy. These tests showed the high efficiency of the developed fault classifier operating in the post-fault mode after compensating the previously detected CS fault and ensuring uninterrupted operation of the drive system. [ABSTRACT FROM AUTHOR]

In this study, the hybrid Taguchi vortex search (HTVS) algorithm, which exhibits a rapid convergence rate and avoids local optima, is employed as a new training algorithm for feed-forward neural networks (FNNs) and its performance was analyzed by comparing it with the vortex search (VS) algorithm, the particle swarm optimization (PSO) algorithm, the gravitational search algorithm (GSA) and the hybrid PSOGSA algorithm. The HTVS-based FNN (FNNHTVS) algorithm was applied to three datasets (iris classification, wine recognition and seed classification) taken from the UCI database (the machine learning repository of the University of California at Irvine) and to the 3-bit parity problem. The obtained statistical results were recorded for comparison. Then, the proposed algorithm was used for fault classification on transmission lines. A dataset was created using 735 kV, 60 Hz, 100 km transmission lines for different fault types, fault locations, fault resistance values and fault inception angles. The FNNHTVS algorithm was applied to this dataset and its performance was tested in comparison with that of other classifiers. The results indicated that the performance of the FNNHTVS algorithm was at least as successful as that of the other comparison algorithms. It has been shown that the FNN model trained with HTVS can be used as a capable alternative algorithm for the solution of classification problems. [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]

This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC. [ABSTRACT FROM AUTHOR]

Integrating inverter-based generators in power systems introduces several challenges to conventional protection relays. The fault characteristics of these generators depend on the inverters' control strategy, which matters in the detection and classification of the fault. This paper presents a comprehensive machine-learning-based approach for detecting and classifying faults in transmission lines connected to inverter-based generators. A two-layer classification approach was considered: fault detection and fault type classification. The faults were comprised of different types at several line locations and variable fault impedance. The features from instantaneous three-phase current and voltages and calculated swing-center voltage (SCV) were extracted in time, frequency, and time–frequency domains. A photovoltaic (PV) and a Doubly-Fed Induction Generator (DFIG) wind farm plant were the considered renewable resources. The unbalanced data problem was investigated and mitigated using the synthetic minority class oversampling technique (SMOTE). The hyperparameters of the evaluated classifiers, namely decision trees (DT), Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Ensemble trees, were optimized using the Bayesian optimization algorithm. The extracted features were reduced using several methods. The classification performance was evaluated in terms of the accuracy, specificity, sensitivity, and precision metrics. The results show that the data balancing improved the specificity of DT, SVM, and k-NN classifiers (DT: from 99.86% for unbalanced data to 100% for balanced data; SVM: from 99.28% for unbalanced data to 99.93% for balanced data; k-NN: from 99.64% for unbalanced data to 99.74% for balanced data). The forward feature selection combined with the Bag ensemble classifier achieved 100% accuracy, sensitivity, specificity, and precision for fault detection (binary classification), while the Adaboost ensemble classifier had the highest accuracy (99.4%), compared to the other classifiers when using the complete set of features. The classification models with the highest performance were further tested using a new dataset test case. They showed high detection and classification capabilities. The proposed approach was compared with the previous methodologies from the literature. [ABSTRACT FROM AUTHOR]

Aiming at the problems of complex and changeable operation conditions, inconsistent size of feature areas, difficulty in extracting and distinguishing faults for wind turbine rolling bearings, a rolling bearing fault diagnosis method (MSCNNSA-BiGRU) was proposed based on a multi-scale convolutional neural network (MSCNN), the self-attention (SA) mechanism and bidirectional gated recurrent unit (BiGRU). Firstly, the multi-scale feature information of the original vibration signal was extracted by MSCNN. Then, the history and future information of the original vibration signal was mined by the BiGRU, and the time sequence feature information of the data was extracted more comprehensively. Meanwhile, SA was introduced to focus on the fault feature to improve the fault diagnosis accuracy of the model. Finally, the feature information was fused into the feature vector and input into the SoftMax layer to achieve fault classification. The proposed method was applied to the actual rolling bearing fault diagnosis of wind turbines. The results show that the fault identification accuracy is up to 92.7% under different working conditions, and the average accuracy is 8.13% higher than that of the classical MSCNN network. Based on the method, the multi-scale time series features from original vibration signals was directly adaptively extracted, “end-to-end” fault diagnosis was achieved, the process of artificial feature extraction was avoided, and the generalization ability and robustness was improved. It has a certain value for practical engineering research and application of rolling bearing fault diagnosis in wind turbines. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the gear failure of the cutting part of the shearer reduced the working efficiency of coal mining production and brought hidden dangers to production safety, taking the MG1000/2500-WD shearer as the research object, the gears of the cutting part of the shearer were analyzed, and the failure mechanism research, simulation analysis and experimental test research were carried out. Firstly, the overall structure of shearer was analyzed, and the common causes and mechanism of gear failure of shearer cutting part were analyzed. Then, the expression formulas of convolution layer, pooling layer and full connection layer of convolution neural network were summarized. The gear fault model of cutting section was constructed based on deep convolution neural network (D-CNN), and the algorithm flow of the model was studied. Finally, by selecting the training data set, the model was trained, and the gear faults of the cutting part were predicted and classified; combining with the performance evaluation index, the experimental results of different models were compared. The experimental results show that the prediction model based on deep learning method can effectively predict the gear fault of shearer cutting part. The gear fault recognition rate is about 98.71%. Under the same conditions, the D-CNN model achieves an accuracy rate of 98.78% and a recall rate of 98.88% for the normal state and fault state of the gear. The model has a more accurate identification rate of gear faults. It has high fault prediction and classification performance. [ABSTRACT FROM AUTHOR]

This article presents a new method for the classification of machine failures using an example of selected generating sets. Measurements and an analysis of the electrical parameters, such as the phase-to-phase voltages at the terminals of a synchronous generator, armature current, and voltage and excitation current of a synchronous generator, are the basis for determining the failure symptoms. The existing energy quality coefficients are adopted as symptoms for the assessment of failures in the monitored generating set. We assume in this method that the description of the input–output relationship is in the form of a black box and use the binary diagnostics matrix (BDM) to investigate the failure–symptom relationships between the inputs (intentional failures) and outputs (failures symptoms = fault-sensitive power quality (PQ) coefficients). The method presented in this article enables the detection and classification of both electrical damage in a synchronous generator and mechanical damage in a diesel engine. It is anticipated that further work and development of the method will focus on the implementation of the algorithm in the form of software into a miniature IoT module for the automatic classification of failures. [ABSTRACT FROM AUTHOR]

Large rotating machinery is widely applied in many industrial applications and shaft orbit classification plays an important role in fault diagnosis of large rotating machinery. Therefore, we propose a novel raw data feature-based automatic and deterministic shaft orbit classification framework for large rotating machinery. The main idea of the proposed framework is that it directly uses the most discriminative geometric features of each shaft orbit type for classification. Different from existing supervised learning shaft orbit classification methods, it does not need any labeled data and it is a deterministic, quite simple, light, easy to implement. Extensive experiments of synchronous integrated period sampling and fixed sampling at different rotating speeds conducted on real and simulated datasets have shown the superiority of proposed framework. This study provides a new deterministic interpretable and explainable approach to support predictive maintenance of large rotating machinery in various industrial fields even when no labeled data is available. [Display omitted] • ADSOC is a raw data feature-based classification framework so that it does not need any labeled data. • ADSOC is invariant to shaft orbit translation, scale and rotation. • ADSOC model and results are easy to interpret and explain. • ADSOC is independent from both shaft orbit starting point and rotating direction, and the shaft orbit does not need to be closed. • ADSOC is a deterministic framework and it is quite simple, light and easy to implement. [ABSTRACT FROM AUTHOR]

Currently, microgrids are becoming more prevalent. Therefore, it is crucial to develop robust and reliable microgrid protection schemes. Researchers have recently explored various approaches to microgrid protection, including adaptive protection and AC microgrid protection. The study offers insights into fault detection and localization in microgrid protection, utilizing precise measurements from the point of common coupling (PCC). It distinguishes between fault occurrences and overload cases, addressing various challenges. Additionally, the analysis explores the role of Linear Discriminant Analysis (LDA) within the framework of multi-machine learning techniques, shedding light on its application in microgrid protection. Ultimately, the aim is to bolster microgrid resilience and reliability for end-users and stakeholders. The system model being tested has been created using Matlab/Simulink software and is based on a real system. The training and testing of the algorithms are developed and evaluated using MATLAB tools. The accuracy of the proposed method is demonstrated in the paper, indicating that it can be used to modernize the current protection apparatus in preparation for the eventual implementation of an advanced microgrid station protection system. • High Accuracy in Fault Identification: DTE and ANN models achieve 100% validation accuracy. • Efficient Overload Differentiation: Superior performance in managing faults and overloads. • Enhanced Fault Localization: Leveraging LDA for improved accuracy in fault localization. • Innovative Voltage Signature Signal: Novel composition enhances signal representation. • Promising Role of LDA: Proficient in fault localization, offering valuable insights. [ABSTRACT FROM AUTHOR]