Fault Classification Algorithm Research Articles

Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

Data-Driven State Prediction and Sensor Fault Diagnosis for Multi-Agent Systems with Application to a Twin Rotational Inverted Pendulum

When a multi-agent system is subjected to faults, it is necessary to detect and classify the faults in time. This paper is motivated to propose a data-driven state prediction and sensor fault classification technique. Firstly, neural network-based state prediction model is trained through historical input and output data of the system. Then, the trained model is implemented to the real-time system to predict the system state and output in absence of fault. By comparing the predicted healthy output and the measured output, which can be abnormal in case of sensor faults, a residual signal can be generated. When a sensor fault occurs, the residual signal exceeds the threshold, a fault classification technique is triggered to distinguish fault types. Finally, the designed data-driven state prediction and fault classification algorithms are verified through a twin rotational inverted pendulum system with leader-follower mechanism.

Fault classification in power distribution systems based on limited labeled data using multi-task latent structure learning

A significant issue for fault classification in power distribution systems is limited fault data for training classifiers to identify power failure types for remediation. Measurement data from power systems are mostly unlabeled without specified fault types, and labeled data with confirmed fault types are very limited, posing challenges to training classifiers with sufficient accuracy. Existing fault classification methods to deal with small labeled samples explore latent structures between labeled and unlabeled data. However, this line of methods has inaccurate assumptions on the relationship between unlabeled and labeled data and suffers from accuracy loss when dealing with very limited data that are labeled. This paper proposes a novel latent structure learning under a multi-task learning framework to supplement information and deal with the challenges in limited labeled data for fault classification. The proposed method not only takes advantage of the latent structure in unlabeled data that are not effectively utilized but also overcomes the limitations of latent structure learning by preventing classifiers from being overfitted to unlabeled data. The method was validated by an experimental study from distribution-level phasor devices in a hardware-in-the-loop testbed compared with state-of-the-art fault classification algorithms. The method is also demonstrated for the robustness against measurement noise.

Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

Fault Detection, Classification and Localization Algorithm for Photovoltaic Array

In this study, analysis of photovoltaic (PV) string currents is performed to understand the behavior of the PV array under faults. The whole analysis is summed up in just two simple statements, and an algorithm is devised on the basis of these statements. The proposed algorithm can detect, classify and localize line-to-ground (L-G) & line-to-line (L-L) faults in the PV array. The proposed method can detect faults of any mismatch level and size of the PV array by using only one current sensor per string. It is also capable of working with finite fault-resistance as well as in the presence/absence of blocking diodes. To check the accuracy of the algorithm, it has been thoroughly tested through experimental setup.

Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays

Automatic detection of solar array faults reduces maintenance costs and increases efficiency. In this paper, we address the problem of fault detection, localization, and classification in utility-scale photovoltaic (PV) arrays using machine learning methods. More specifically, we develop a series of customized neural networks for detection and classification of solar array faults. We evaluate fault detection and classification using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN). We examine and assess the use of customized neural networks with dropout regularizers. We develop and evaluate neural network pruning strategies and illustrate the trade-off between fault classification model accuracy and algorithm complexity. Our approach promises to elevate the performance and robustness of PV arrays and compares favorably against existing methods.

Fault classification and fault area detection in GUPFC-compensated double-circuit transmission lines based on the analysis of active and reactive powers measured by PMUs

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.

Open-Circuit Fault Diagnosis of Three-Phase PWM Rectifier Using Beetle Antennae Search Algorithm Optimized Deep Belief Network

Effective open-circuit fault diagnosis for a two-level three-phase pulse-width modulating (PWM) rectifier can reduce the failure rate and prevent unscheduled shutdown. Nevertheless, traditional signal-based feature extraction methods show poor distinguishability for insufficient fault features. Shallow learning diagnosis models are prone to fall into local extremum, slow convergence speed, and overfitting. In this paper, a novel fault diagnosis strategy based on modified ensemble empirical mode decomposition (MEEMD) and the beetle antennae search (BAS) algorithm optimized deep belief network (DBN) is proposed to cope with these problems. Initially, MEEMD is applied to extract useful fault features from each intrinsic mode function (IMF) component. Meanwhile, to remove features with redundancy and interference, fault features are selected by calculating the importance of each feature based on the extremely randomized trees (ERT) algorithm, and the dimension of fault feature vectors is reduced by principal component analysis. Additionally, the DBN stacked with two layers of a restricted Boltzmann machine (RBM) is selected as the classifier, and the BAS algorithm is used as the optimizer to determine the optimal number of units in the hidden layers of the DBN. The proposed method combined with feature extraction, feature selection, optimization, and fault classification algorithms significantly improves the diagnosis accuracy.

Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell

This paper develops a model-based data driven algorithm for fault classification in proton exchange membrane fuel cells (PEMFCs). The proposed approach overcomes the drawbacks of voltage and current density assumptions in conventional model-based fault identification methods and data limitations in existing data driven approaches. This is achieved by developing a 3D model of fuel cells (FC) based on semi empirical model, analytical representation of electrochemical model, thermal model, and impedance model. The developed model is simulated for membrane drying and flooding faults in PEMFC and their effects are identified for the action of varying temperature, pressure, and relative humidity. The ohmic, concentration, activation and cell voltage losses for the simulated faults are observed and processed with wavelet transforms for feature extraction. Furthermore, the support vector machine learning algorithm is adapted to develop the proposed fault classification approach. The performance of the developed classifier is tested for an unknown data and calibrated through classification accuracy. The results showed 95.5% training efficiency and 98.6% testing efficiency.

Data-Driven Open-Set Fault Classification of Residual Data Using Bayesian Filtering

Data-driven fault classification in industrial applications is complicated by unknown fault classes and limited training data. In addition, different faults can have similar effects on sensor outputs resulting in fault classification ambiguities, i.e., multiple fault hypotheses can explain the data. One solution is to identify and rank all plausible fault classes that give useful information, for example, at a workshop when performing troubleshooting. A probabilistic fault classification algorithm is proposed for residual data classification combining the Weibull-calibrated one-class support vector machines for fault class modeling and Bayesian filtering for time-series analysis. The fault classifier ranks different fault classes and can identify sequences from unknown fault realizations, i.e., faults not represented in training data. Real residual data computed from sensor data and model analysis of an internal combustion engine are used as a case study illustrating the usefulness of the proposed method.

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

PurposeFor the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approachThe algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.FindingsThe experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/valueThe proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

A Novel Fault Classification Approach for Photovoltaic Systems

Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.

A knowledge-based diagnosis algorithm for broken rotor bar fault classification using FFT, principal component analysis and support vector machines

A knowledge-based diagnosis algorithm for broken rotor bar fault classification using FFT, principal component analysis and support vector machines

A knowledge-based diagnosis algorithm for broken rotor bar fault classification using FFT, principal component analysis and support vector machines

Despite their ruggedness and reliability, induction motors experience faults due to stresses and manufacturing errors. Early detection of these faults is important in preventing further damages and minimising down-time. In this study, a machine learning algorithm is proposed for detection and classification of broken rotor bar (BRB) faults according to their severity. Removal of high frequency components then amplification was performed on the measured single-phase current. Features were then extracted using FFT and principal component analysis (PCA). Support vector machines (SVM) was used for classification. Two classification schemes were analysed; one classifying in one step and another in two steps. Experiments were performed to evaluate the algorithms by analysing their recognition rates. Six different SVM kernels were studied. Recognition rates as high as 97.9% were achieved. False negative rate as low as 0% was also realised. Furthermore, it was found out that using more principle components does not yield significant improvements.

A Generalized Switch Fault Diagnosis for Cascaded H-Bridge Multilevel Inverters Using Mean Voltage Prediction

A cascaded H-bridge multilevel inverter is an indispensable part of high-power industrial drives. The multilevel inverters have an increased chance of switch fault as a consequence of a considerably large number of semiconductor switches associated in the power conversion. A quick and precise fault detection–isolation strategy improves the system reliability as well as reduces the shutdown time of the drive. This article presents a novel generalized open-switch fault-diagnostic approach for an $N$ -level cascaded H-bridge multilevel inverter. In this detection technique, half-cycle mean values of bridge voltages, which are calculated for positive and negative half cycles individually, are used as fault identification features. These means under open-switch fault are predicted from the reference half-cycle means and compared with those measured values to locate the open-switch fault. This quick detection scheme can identify the faulty switch within one fundamental period of output voltage for the cascaded inverters having different number of voltage levels. The computation requirement is minimum, since this approach does not need complex calculations or domain transformations. This strategy can efficiently locate the faulty switch of the cascaded inverter working with various level-shifted pulsewidth modulation schemes, different loading conditions, modulation indexes, and switching frequencies. The potency of the fault classification algorithm is verified through simulation and experimentation.

Fault classification and location identification in a smart DN using ANN and AMI with real‐time data

This paper presents a real-time fault classification and location identification method for a smart distribution network (DN) using artificial neural networks (ANNs) and advanced metering infrastructure (AMI). It also describes the development of a testbed for real-time testing of the proposed approach. The testbed consists of a simulated power system model [running on a digital real-time simulator (DRTS)] and AMI. The core parts of AMI are smart meters (SMs), a communication network (developed using DNP3 protocol over transfer control protocol/Internet protocol), data concentrator (DC), and a Utility Operations Centre (UOC). Event-driven data from SMs are collected in the DC and then fed to the UOC for being used as inputs for the novel ANN-based fault classification and location identification algorithm. On the basis of the data received, the algorithm can classify the fault type and locate it with high accuracy. Both balanced and unbalanced fault types are tested on different nodes and lines throughout a DN modelled in offline and on the DRTS. A comprehensive sensitivity analysis is performed to validate the effectiveness of the proposed method. Classification accuracy of over 99% is achieved when classifying all fault types, and above 95% accuracy is achieved when identifying the fault location.

Medical rolling bearing fault prognostics based on improved extreme learning machine

The problem studied in this article:the random selection of the input weight and the implicit layer bias of the extreme learning machine leads to the instability of the medical rolling bearing fault prediction result of the algorithm. It requires more hidden layer nodes to ensure the accuracy of the algorithm, duing to this, Ensemble Error Minimized Extreme Learning Machine (EEM-ELM) is proposed. The EEM-ELM uses various error limit learning machines (EM-ELM) trained on different training sets as member classifiers. Member classifier factors are also used, including predictive entropy, which verifies the set’s accuracy and average and output weights as weights. All of these form a composite classifier by weighted linear combination. This method skillfully solves the problem of optimal hidden layer neurons number selection. The normalized energy and permutation entropy of each IMF component obtained by VMD decomposition fault signal are established as feature vectors, and the improved EEM-ELM algorithm is used as the fault diagnosis model for bearing fault classification algorithm. The fault diagnosis model is applied to the classification of bearing fault signals. The analysis of experimental data proves that the classification result of the proposed EEM-ELM algorithm is better than the ELM algorithm. At the same time, the accuracy rate is higher than each member classifier because of proper weighting processing. Apart from this, since the EEM-ELM algorithm integrates the error minimization limit learning machine, the EEM-ELM algorithm does not need to select the optimal hidden layer node number. The EEM-ELM algorithm only needs to specify the maximum number of training set samples that each EM-ELM-based classifier can tolerate misclassification to achieve high stability and high accuracy classification.

Transmission Line Fault Detection, Classification and Location using Wavelet Transform

This paper present a new algorithm for fault detection, classification and location of overhead transmission line using Wavelet Transform (WT) based Discrete Wavelet Transform (DWT) is proposed. The different system faults such as LG, LLG and LLLG in transmission line should be detect, classify and locate rapidly. The proposed method is based on the voltage and current signal information from the power model in MATLAB to generate the transient voltage and current signal in both time and frequency domain. DWT using “db6” as a mother wavelet is used to capture transient current signals and extract the high frequency detail coefficient for detecting and classifying the fault disturbance. The classification process is based on ground threshold value. The location of faults is carried out by obtaining the fault information from source terminal end to remote terminal end along with the total transmission line length. The proposed method is tested on the MATLAB/SIMULINK environment with the Simulink model. In simulation process the proposed algorithm achieved the fault detection, classification and locate all eleven types of possible fault in transmission line and the result are compared with the AR and MED method.

Hybrid Signal Processing and Machine Learning Algorithm for Adaptive Fault Classification of Wind Farm Integrated Transmission Line Protection

The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed lead to the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High Voltage Transmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration.