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The low fault current contribution from inverter-interfaced distributed generators (IIDGs) and bidirectional flow of current in the power networks introduces protection challenges when islanded. The existing fault detection scheme based on the transient monitoring function (TMF) fails under such scenarios. A new decentralised machine learning (ML) based fault detection and classification scheme is proposed in this paper using terminal voltage information of IIDG (v o−abc). TMF of v o−abc , along with the zero sequence component of v o−abc , is utilised for training and testing of ensemble bagged trees ML algorithm. The proposed scheme is implemented inside each IIDG, and hence, it does not require communication. It is validated on a modified CIGRE test benchmark microgrid for all shunt faults at various locations with different loading, fault resistances, and inception angles. The accuracy of detection and classification of fault (DCF) during fault cases with unbalanced loads, noise, harmonic loads, and no-fault scenarios such as load and DG switching proves the effectiveness of the proposed method with varying scenarios. The performance of the proposed scheme is found to be superior when compared with the existing threshold based TMF and Hilbert–Huang transform (HHT) based techniques considering accuracy, dependability, security, and response time. • Queries by all the reviewer 3 are addressed in details. • Made suitable changes to the introduction to bring in more clarity. [ABSTRACT FROM AUTHOR]

With data in industrial processes being larger in scale and easier to access, data-driven technologies have become more prevalent in process monitoring. Fault classification is an indispensable part of process monitoring, while machine learning is an effective tool for fault classification. In most practical cases, however, the number of fault data is far smaller than normal data, and this imbalance of dataset would lead to the significant decline in performance of common classifier learning algorithms. To this issue, we propose a data augmentation method, which is based on Generative Adversarial Networks(GAN) and aided by Gaussian Discriminant Analysis(GDA), for enhancement of fault classification accuracy. To validate the effectiveness of this method for imbalanced fault classification, on toy data and the Tennessee Eastman (TE) benchmark process, common oversampling method and the basic GAN are compared to our method, with different classification algorithms. Besides, proposed method is deployed and parallelly trained on Tensorflow platform, which is suitable for applications like data augmentation and imbalanced fault classification in industrial big data environments. • A data augmentation method for imbalanced fault classification. • Generative Adversarial Networks aided by Gaussian Discriminant Analysis. • Superiorities of the model are analyzed and discussed in detail. • Parallel implementation of the model for big data in Tensorflow. • The performance of the method is verified on both toy data and TE process. [ABSTRACT FROM AUTHOR]

Stacked auto-encoder (SAE)-based deep learning has been introduced for fault classification in recent years, which has the potential to extract deep abstract features from the raw input data. However, SAE cannot ensure the relevance of deep features with the fault types due to its unsupervised self-reconstruction in the pretraining stage. To overcome this problem, a stacked supervised auto-encoder is proposed to pretrain the deep network and obtain deep fault-relevant features from raw input data. In each supervised auto-encoder, informative features are learned from the input data with the goal that they can largely distinguish different fault types. By stacking multiple supervised auto-encoders hierarchically, high-level fault-relevant features are gradually learned from raw input data, which can improve the classification accuracy of the classifiers. The proposed SSAE is tested on the Tennessee–Eastman (TE) benchmark process and a real industrial hydrocracking process. The results show the effectiveness and flexibility of SSAE. • A stacked supervised auto-encoder is proposed to pretrain deep network and obtain deep fault-relevant features. • In each supervised auto-encoder, informative features are learned from the input data with the goal that they can largely distinguish different fault types. • High-level fault-relevant features are gradually learned from raw input data by hierarchically stacking multiple supervised auto-encoders. • High classification performance of the proposed method is validated on TE process and an industrial hydrocracking process. [ABSTRACT FROM AUTHOR]

• K-means Bayes algorithm is newly proposed for imbalanced fault classification. • The proposed method neither adds samples nor reduces samples, ensuring the quality of the training data. • A MapReduce form of the proposed algorithm is further developed for big data application. • The superiority of the developed method is tested on a benchmark process. Fault classification is an important part of process monitoring in industrial processes. Most conventional fault classification methods are under the assumption that the amount of data in different classes are similar. However, in practice, most of the data collected from industrial process are normal data (majority) and only a few of them are fault data (minority). In other words, fault classification can be seen as an imbalanced data classification problem, which has not been considered in this area to date. In this paper, a K-means Bayes algorithm is proposed to deal with the imbalanced fault classification problem. After that, a MapReduce approach is further introduced to implement the method for fault classification in the big data case. Effectiveness of the proposed method is verified through experiments based on Tennessee Eastman (TE) benchmark process. [ABSTRACT FROM AUTHOR]

Residential air-conditioning systems are subject to soft faults, which can reduce their efficiency, capacity, and life expectancy, without being detected by the occupants. Early detection and diagnosis of faults could potentially be done by using machine learning (ML) on measurable features of the air-conditioner, such as refrigerant temperatures and pressures. This paper describes a novel approach to applying ML, which is to train a fault classifier using data from one system, and apply it to different systems. If this is effective, it can significantly reduce the cost of widescale application of ML fault classification. The support vector machine (SVM) algorithm was used. An additional question for practical application is whether the number of sensors required for input feature measurement can be reduced. This work also tests the feature reduction effect, in combination with applying an ML classifier trained on one system, to classify faults in two other systems. The results are disappointing, showing that fault classification is poor when the SVM classifier is trained on a different system than it is applied to. However, with additional work, this method has potential for improvement, possibly to a point where it can be a viable approach for field deployment. [ABSTRACT FROM AUTHOR]

In this work, a new fault classification and phase selection methodology for half-wavelength (HWL) transmission lines is developed. Initially, the basic principles of phase currents transformations are presented. Then, an innovative faulted phase selection methodology based on the variation of those currents is developed. The behavior of the electrical quantities related to short circuits in such long lines is very different when compared to conventional lines. The faulted phase selection method employed in this work is built on the fact that those decoupled currents are expected to not vary or vary less than the others, depending on how many phases are in short circuit. The robustness of the method was analyzed considering different load conditions and fault resistances. Furthermore, different sampling rates were considered for the phase currents. Extensive simulation studies showed that the developed method can accurately identify the fault types and the faulted phase with a relatively low sample rate. Moreover, the simulation results showed that the proposed method is not affected by the presence of inter-harmonics and responds precisely with high-resistance faults. The proposed algorithm presented errors smaller than 0.04%. • A new method for ground and phase fault classification based on decoupled currents by means of modal transformation was developed in this study. • The decoupled currents are obtained by means of a modal transformation. • The developed method is robust to faults with high resistance. • The developed method is not affected by the presence of interharmonics. [ABSTRACT FROM AUTHOR]

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature. Artificial neural networks (ANNs) and wavelet transform (WT) have drawn significant attention lately, but they present some drawbacks when dealing with power systems faults where data are often contaminated by noise. This paper proposes an approach by combining independent component analysis (ICA) with travelling wave (TW) theory and support vector machine (SVM). The approach is adequate to locate and recognize faults in high-voltage (HV) transmission lines, while the acquired signals are noisy. Experiments performed for distinct types and locations of faults in a real transmission line model have shown that the proposed combined methods are able to provide excellent performance in fault location. The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise. It can be stated that this method presents better performance than those regarding the main conventional techniques such as wavelets and neural networks in the presence of noise. [ABSTRACT FROM AUTHOR]

The conventional distance relaying algorithms are unable to detect the inter-circuit faults, cross-country faults, high resistance faults which may occur in a double circuit line. This paper presents combined Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) based directional relaying and fault classification scheme including inter-circuit faults, cross-country faults and high resistance faults. SVM modules are designed for forward or reverse fault identification and fault classification using single terminal data. The 3rd level approximate discrete wavelet transform coefficients of three phase current signals only have been used. Proposed method is tested with variations in fault type, fault location, fault inception angle, fault resistance, inter-circuit faults, and cross-country faults. The proposed method based on SVM does not need any threshold to operate which is an exceptional attribute for a protective function. As SVMs are not based on comparing with some threshold, rather initially the SVMs are trained with the wide variety of fault patterns which is an offline process and then the trained SVMs are tested online to detect and classify the fault within short time. The test results show that all types of shunt faults can be identified within half cycle time. The proposed scheme offers both primary protection to 95% of the line section and also backup protection to 95% of the adjacent reverse and forward line section also. [ABSTRACT FROM AUTHOR]

Since industrial process data often presents strong correlations, high complexity, and nonlinear patterns, a proficient deep learning model is required for the fault classification task. Recent researches have shown that deep learning models like stacked autoencoder (SAE) are able to learn deep abstract features from complex process data. Nevertheless, a traditional SAE cannot extract the informative fault-relevant features and data distribution features from industrial process data, which are necessary for effective fault classification in industrial processes. Thus, this study proposes a semi-supervised Inter-Relational Mahalanobis SAE (IRM-SAE) model to learn inter-relational distribution and fault-relevant dynamic features of process data for fault classification. First, the Inter-Relational Mahalanobis loss is introduced into the original objective function of SAE to learn meaningful inter-relational distribution features within the data. Then, an active time frame technique is developed to preprocess the input data to capture the dynamic features of the data. Furthermore, to fully utilize both labeled and unlabeled data in industrial processes, the semi-supervised strategy is introduced to learn fault-related features for better fault classification. To validate the performance of the proposed model, it is applied on the Tennessee–Eastman process and a real-world industrial hydrocracking process. The experimental results show that the proposed model has higher fault classification performance compared to other deep learning models. • A novel semi-supervised Inter-Relational Mahalanobis SAE (IRM-SAE) model is proposed to learn inter-relational distribution and fault-relevant dynamic features of process data. • An active time frame technique is developed to preprocess the process data to capture the dynamic features of the data. • To fully utilize both labeled and unlabeled data in industrial processes, the semi-supervised strategy is introduced to learn fault-related features for better fault classification. • The experimental results on two industrial processes show that the proposed model has higher fault classification performance compared to other deep learning models. [ABSTRACT FROM AUTHOR]

As substantial parts of process monitoring, fault classification techniques have been widely utilized in modern industries. However, most methods can only perform well under specific condition, which indicates that it is always difficult to ensure the classification efficiency for complex industrial processes using only one method. In this paper a weighted decision fusion system is proposed where an extended analytic hierarchy process (EAHP) structure is designed to give a comprehensive explanation for the weights assigned to the original fault classification results. Firstly, the fault category information is embedded in the EAHP structure, which makes it possible to consider both fault-wise and classifier-wise information in the fault classification. Secondly, an overall priority (OP) matrix is proposed to provide full prior knowledge for all classifiers. Different from previous researches, traditional OP vectors are replaced by the new-designed OP matrix which can contain more information about fault category. Thirdly, another confidence matrix is carried out to update the classification results. Compared with previous state-of-art, the confidence matrix can be adjusted according to the specific original classification result Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee Eastman process (TEP) where the proposed decision fusion system shows superior fault classification results in both experiments. [ABSTRACT FROM AUTHOR]

The paper presents the construction and application of multiple classifiers to increase the accuracy of the fault detection module in the diagnostic task. The structure of the ensemble of classifiers is presented and the applied voting mechanisms explained. Methods of storing knowledge in the intelligent diagnostic systems are introduced and their taxonomy provided. Next, the selected algorithms implemented in the fault detection operation are briefly described. Problems with the practical implementation of the proposed solution are considered. The scheme is used to detect faults in the analog part of the MEMS accelerometer. The paper is concluded with the possible prospects for the proposed scheme. [ABSTRACT FROM AUTHOR]

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

Recently, deep learning has become a popular tool for fault detection and diagnosis in chemical processes to learn complex nonlinear features. However, the features extracted from most traditional deep networks are only good representation for the raw input data, which may contain irrelevant information with the faults and are not beneficial for the fault classification task. Thus, a novel classification-driven neuron-grouped stacked autoencoder (CG-SAE) is proposed for hierarchical fault-related feature representation in this paper. During the pre-training phase of CG-SAE, the hidden neurons in each CG-AE are divided into several groups, the number of which is equal to that of the data categories. Each group is mainly associated to one type of fault. Moreover, the supervised information is introduced without additional weights by comparing the error between the actual label and the average activation of the corresponding group. In this way, the extracted deep features can contain category-related information and be clustered to different categories. In terms of the classification accuracy and training efficiency, CG-SAE performs better than some advanced supervised methods as verified by the Tennessee Eastman (TE) benchmark and an industrial hydrocracking process. [ABSTRACT FROM AUTHOR]

Abstract: This paper exploits the Wavelet Transform (WT) analysis along with Artificial Neural Networks (ANN) for the diagnosis of electrical machines winding faults. A novel application is presented exploring the problem of automatically identifying short circuits of windings, which often appear during machine manufacturing and operation. Such faults are usually resulting from electrodynamics forces generated during the flow of large short circuit currents, as well as forces occurring when the machines are transported. The early detection and classification of winding failures is of particular importance, as these kinds of defects can lead to winding damage due to overheating, imbalance, etc. Application results and investigations of windmill generator winding turn-to-turn faults between adjacent winding wires are presented. The ANN approach is proven effective in detecting and classifying faults based on WT features extracted from high frequency measurements of the admittance, current, or voltage responses. [Copyright &y& Elsevier]

Many automated sound-based fault diagnosis or classification methods have been presented in the literature. A novel automatic fault diagnosis method is presented by using sounds for the cooling system of the data center. A novel feature generator and an iterative feature selector are used together to present an automated data center cooling system (DCCS) fault diagnosing method. A new feature generator is proposed inspired by knitting hence, it is called a local knit pattern (LKP). A multiple pooling based decomposition method is presented as a preprocessor. The LKP generates features from each signal. Iterative neighborhood component analysis (INCA) feature selector selects the most discriminative. Twelve classifiers are calculated in the classification phase. The selected classifiers were achieved greater than 90.0% classification accuracies, and the best-resulted classifier is Quadratic SVM. It reached 96.40% classification accuracy. Results show that new generation automated sound fault diagnosis applications can also be developed as novel sound-based fault detection applications. [ABSTRACT FROM AUTHOR]

• Novel fault classification algorithm (FCA) for GUPFC-compensated transmission lines. • FCA is based on the loci of active and reactive powers calculated using PMU signals. • Presenting faulty area and faulty circuit detection algorithms using new indices. • The proposed method is robust and independent of the operating modes of the GUPFC. • Easily applicable in regular fault locators available in the protection industry. The paper presents a fault area detection and classification algorithm in the phasor domain for double-circuit lines compensated by a generalized unified power flow controller (GUPFC). In the first step, by defining and using a voltage index, the algorithm determines the side of the GUPFC in which the fault has occurred. In the second step, by defining and employing a current index, the algorithm will be able to identify the circuit of the line on which the fault has happened. Finally, the fault is classified. In this step, the algorithm detects the faulty phase by analyzing the loci of three-phase active and reactive powers at the line terminal. The algorithm uses the current and voltage phasors of the line terminals measured by the phasor measurement units (PMUs) in one cycle before and during the fault. The GUPFC and the test system have been modeled in MATLAB/Simulink. The presented algorithm is tested and evaluated under different conditions including different fault types and fault resistances for all normal series and shunt fault types. The results obtained from simulations verify the accurate performance of the algorithm for various fault conditions. [ABSTRACT FROM AUTHOR]

• A systematic review for Smart Grid systems faults. • A classification framework on relevant grid requirements. • Future developments related to fault detection and classification. • Advance Metering Infrastructure under the perspective of power faults. Smart Grid (SG) is a multidisciplinary concept related to the power system update and improvement. SG implies real-time information with specific communication requirements. System reliability relies on the best capabilities for monitoring and controlling the grid. Among other aspects, SG applications involve three main challenges, sufficient real-time capable measurement units, managing large data sets, and two-way low-latency communications. Considering fault detection and classification a key factor to SG reliability, this work provides a systematic review of SG faults from the most significant research databases and state-of-the-art research papers aiming at creating a comprehensive classification framework on the relevant requirements. This paper includes in detail the classification of different fault scenarios in a comprehensive framework that involves system-level of application, e.g. , transmission, distribution, commercial, DG, and EV. To this end, We analyze and indicate relevant topics for future developments related to the monitoring and fault detection and classification in SG systems. [ABSTRACT FROM AUTHOR]

This work deals with robust dynamic probabilistic modeling and fault classification for process data. In dynamic processes, observed variables can be numerous in amount and correlated with each other in both variable-wise and time-wise. While multivariate statistical analysis methods such as principle component analysis (PCA) are widely used to handle variable-wise correlations and also for dimension reduction purpose, the time-wise correlation among process data is typically addressed by constructing dynamical models, e.g. time-series data model, state space model, etc. In this paper, the robust probabilistic principle component analysis (RPPCA) model is introduced for feature extraction from contaminated process data, suffered from outliers, disturbances, heave noises, etc. A dynamic Bayesian network (DBN) is then constructed, with incorporation of a mixture of Gaussian components for approximation of the non-Gaussian characteristics of latent variables. Based on the developed robust dynamic Bayesian network model, a fault classification scheme is proposed. To validate the effectiveness and feasibility of the new method, two well-known benchmark case studies are carried out. Simulation results show that robust dynamic method performs better in outlier contaminated situations, where the performance has been improved by 10%–20% in most cases. [ABSTRACT FROM AUTHOR]