scholarly journals Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid

2020 ◽  
Vol 10 (14) ◽  
pp. 4965
Author(s):  
Yordanos Dametw Mamuya ◽  
Yih-Der Lee ◽  
Jing-Wen Shen ◽  
Md Shafiullah ◽  
Cheng-Chien Kuo
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Radial Distribution ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Efficiency Coefficient ◽  
Distribution Grid ◽  
Phase Current ◽  
Three Phase ◽  
Types Of Faults

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.

Modal Current and Cumulative Sum Based Fault Detection Approach in Transmission Lines

2018 ◽  
Vol 19 (6) ◽  
Author(s):  
Mohammad Amin Jarrahi ◽  
Haidar Samet
Keyword(s):  
Fault Detection ◽  
Transmission Lines ◽  
Fault Analysis ◽  
Cumulative Sum ◽  
Efficiency Assessment ◽  
Fast Approach ◽  
Detection Approach ◽  
Three Phase ◽  
Different Types ◽  
Types Of Faults

AbstractIn this paper, a simple and fast approach is suggested for fault detection in transmission lines. The proposed technique utilizes a modified cumulative sum approach for a modal current to identify faults. The modal current is derived by proper linear mixing of three-phase currents. Since different types of faults may occur in transmission lines, all three-phase currents should be considered during fault analysis. By converting three-phase currents to a modal current, the processing time is reduced and less memory is needed. In this paper, a modal current is processed instead of three-phase currents. The modified cumulative sum approach presented in this paper is capable of decreasing computational burdens on the digital relay and accelerating the fault detection procedure. The proposed fault detection technique is evaluated in four different systems. Moreover, some real recorded field data were deliberated in the efficiency assessment of the proposed method. The results denote high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods from different aspects.

scholarly journals Switch Open-Fault Detection for a Three-Phase Hybrid Active Neutral-Point-Clamped Rectifier

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1437
Author(s):  
Sang-Hun Kim ◽  
Seok-Min Kim ◽  
Sungmin Park ◽  
Kyo-Beum Lee
Keyword(s):  
Fault Detection ◽  
Reactive Power ◽  
Detection Method ◽  
Field Effect Transistors ◽  
Individual Characteristics ◽  
Neutral Point ◽  
Phase Current ◽  
Three Phase ◽  
Open Switch ◽  
Open Faults

This paper proposes a fault-detection method for open-switch failures in hybrid active neutral-point-clamped (HANPC) rectifiers. The basic HANPC topology comprises two SiC-based metal-oxide-semiconductor field-effect transistors (MOSFETs) and four Si insulated-gate bipolar transistors (IGBTs). A three-phase rectifier system using the HANPC topology can produce higher efficiency and lower current harmonics. An open-switch fault in a HANPC rectifier can be a MOSFET or IGBT fault. In this work, faulty cases of six different switches are analyzed based on the current distortion in the stationary reference frame. Open faults in MOSFET switches cause immediate and remarkable current distortions, whereas, open faults in IGBT switches are difficult to detect using conventional methods. To detect an IGBT fault, the proposed detection method utilizes some of the reactive power in a certain period to make an important difference, using the direct-quadrant (dq)-axis current information derived from the three-phase current. Thus, the proposed detection method is based on three-phase current measurements and does not use additional hardware. By analyzing the individual characteristics of each switch failure, the failed switch can be located exactly. The effectiveness and feasibility of the proposed fault-detection method are verified through PSIM simulations and experimental results.

scholarly journals Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

2020 ◽  
Vol 10 (15) ◽  
pp. 5251 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motors ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Bearing Fault ◽  
Ensemble Machine Learning

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

Faults Classification Scheme for Three Phase Induction Motor

2014 ◽  
Vol 3 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Mohammed Obaid Mustafa ◽  
George Nikolakopoulos ◽  
Thomas Gustafsson
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Short Circuit ◽  
Real Life ◽  
Classification Algorithm ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Linear Discrimination ◽  
Three Phase ◽  
Detection And Diagnosis

In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed classification algorithm is applied to detect of two kinds of induction machine faults, which area) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. And thus a clear classification of the healthy and the faulty cases can be robustly performed, by having the optimal hyper plane. This method can separate the feature current classes in a low dimensional subspace. Robust linear discrimination has been one of the most widely used fault detection methods in real-life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time applies a straight-forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results in different fault types.

scholarly journals A Novel Technique for Rotor Bar Failure Detection in Single-Cage Induction Motor Using FEM and MATLAB/SIMULINK

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Seyed Abbas Taher ◽  
Majid Malekpour
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Failure Detection ◽  
Discrete Wavelet ◽  
Analogue Model ◽  
Matlab Simulink ◽  
Start Up ◽  
Three Phase ◽  
State Regime ◽  
Cage Induction Motor

In this article, a new fault detection technique is proposed for squirrel cage induction motor (SCIM) based on detection of rotor bar failure. This type of fault detection is commonly carried out, while motor continues to work at a steady-state regime. Recently, several methods have been presented for rotor bar failure detection based on evaluation of the start-up transient current. The proposed method here is capable of fault detection immediately after bar breakage, where a three-phase SCIM is modelled in finite element method (FEM) using Maxwell2D software. Broken rotor bars are then modelled by the corresponding outer rotor impedance obtained by GA, thereby presenting an analogue model extracted from FEM to be simulated in a flexible environment such as MATLAB/SIMULINK. To improve the failure recognition, the stator current signal was analysed using discrete wavelet transform (DWT).

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