scholarly journals Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1203 ◽  
Author(s):  
Chaichan Pothisarn ◽  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul ◽  
Chaiyan Jettanasen ◽  
Dimas Anton Asfani ◽  
...  
Keyword(s):  
Power System ◽  
Fault Location ◽  
Classification Algorithm ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
System Protection ◽  
Fault Type ◽  
Zero Sequence ◽  
Mother Wavelets

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.

scholarly journals Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

2020 ◽  
Vol 10 (11) ◽  
pp. 3967 ◽  
Author(s):  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Transmission Line ◽  
Fault Location ◽  
Back Propagation ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Hybrid Transmission

This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.

scholarly journals Short Circuit Faults Identification and Localization in IEEE 34 Nodes Distribution Feeder Based on the Theory of Wavelets

2018 ◽  
Vol 14 (1) ◽  
pp. 65-79
Author(s):  
Sara Authafa
Keyword(s):  
Fault Location ◽  
Short Circuit ◽  
Circuit Breaker ◽  
Discrete Wavelet ◽  
Detection Scheme ◽  
Protection Scheme ◽  
System Protection ◽  
Fault Type ◽  
Distribution Feeder ◽  
System States

In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package.

scholarly journals Fault Classifications in Distribution Systems Consisting of Wind Power as Distributed Generation Using Discrete Wavelet Transforms

2019 ◽  
Vol 11 (24) ◽  
pp. 7209
Author(s):  
Theerasak Patcharoen ◽  
Atthapol Ngaopitakkul
Keyword(s):  
Wind Power ◽  
Distribution System ◽  
Wavelet Transforms ◽  
Distribution Systems ◽  
Fault Location ◽  
Classification Algorithm ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Fault Type ◽  
Signal Characteristics

This paper proposed a fault type classification algorithm in a distribution system consisting of multiple distributed generations (DGs). The study also discussed the changing of signal characteristics in the distribution system with DGs during the occurrence of different fault types. Discrete Wavelet Transform (DWT)-based signal processing has been used to construct a classification algorithm and a decision tree to classify fault types. The input data for the algorithm is extracted from the three-phase current signal under normal conditions and during fault occurrence. These signals are recorded from the substation, load, and DG bus. The performance of the proposed classifying algorithm has been tested on a simulation system that was modeled after part of Thailand’s 22 kV distribution system, with a 2-MW wind power generation as the DG, connected to the distribution line by PSCAD software. The parameters that were taken into consideration consisted of the fault type, location of the fault, location of DG(s), and the number of DGs, to evaluate the performance of the proposed algorithm under various conditions. The result of the simulation indicated significant changes in current signal characteristics when installing DGs. In addition, the proposed algorithm has achieved a satisfactory accuracy in terms of identifying and classifying fault types when applied to a distribution system with multiple DGs.

A Comparison Study of Learning Algorithms for Estimating Fault Location

2017 ◽  
Vol 6 (2) ◽  
pp. 464 ◽  
Author(s):  
Mimi Nurzilah Hashim ◽  
Muhammad Khusairi Osman ◽  
Mohammad Nizam Ibrahim ◽  
Ahmad Farid Abidin ◽  
Ahmad Asri Abd Samat
Keyword(s):  
Transmission Lines ◽  
Conjugate Gradient ◽  
Goodness Of Fit ◽  
Fault Location ◽  
Discrete Wavelet ◽  
Training Algorithm ◽  
System Protection ◽  
Fault Resistance ◽  
Quasi Newton ◽  
Selection Of

Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current signal acted as traveling wave fault location technique. Six types commonly used backpropagation training algorithm were selected including the Levenberg-Marquardt, Bayesian Regulation, Conjugate gradient backpropagation with Powell-Beale restarts, BFGS quasi-Newton, Conjugate gradient backpropagation with Polak-Ribiere updates and Conjugate gradient backpropagation with Fletcher-Reeves updates. The proposed fault location method is tested with varying fault location, fault types, fault resistance and inception angle. The performance of each training algorithm is evaluated by goodness-of-fit (R<sup>2</sup>), mean square error (MSE) and Percentage prediction error (PPE). Simulation results show that the best of training algorithm for estimating fault location is Bayesian Regulation (R<sup>2 </sup>= 1.0, MSE = 0.034557 and PPE = 0.014%).

scholarly journals Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Moez Ben Hessine ◽  
Souad Ben Saber
Keyword(s):  
Power Systems ◽  
Comparative Study ◽  
Transmission Lines ◽  
Fault Location ◽  
Fault Classification ◽  
Fault Type ◽  
Three Phase ◽  
Instantaneous Current ◽  
Artificial Neural ◽  
Extra High Voltage

The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented; the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms.

scholarly journals Simultaneous fault detection and diagnosis in electric power system using hybrid method

2018 ◽  
Vol 7 (4) ◽  
pp. 2692
Author(s):  
Dr. Afaneen anwar ◽  
Rana Ali Abttan
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Measurement Method ◽  
Fault Location ◽  
Current Measurement ◽  
Electric Power System ◽  
Fault Detection And Diagnosis ◽  
System Stability ◽  
Fault Classification ◽  
Impedance Method

Simultaneous fault is one of the challenging issues. Faults are the major hurdles in power system designing and protection .Simultaneous fault is the combination of faults indicates that that two or more faults which occur at the same time.The main objective of simultaneous fault detection, classification and location is satisfy accelerates line restoration, maintains system, stability, repairs the fault, decreases the restoration time and increases the system reliability.This paper presents an approach for analysis, detection, classification and location for simultaneous faults in bus bar and transmission line. Two port network is adapted for analysis , voltage and current measurement method is adapted in the fault detection, neural network in the fault classification and location for different types of fault and places were to estimation accurately fault location by analyzing the data available after the beginning of disturbance.All programs were written in MATLAB environment. The programs were test on IEEE- 11 bus bar network. The results clarified that the voltage and current measurement method and impedance method is very effective for simultaneous fault detection, classification and location.  

Classifying the Fault Type in Underground Distribution System Based on Fuzzy Logic Algorithm

2017 ◽  
Vol 8 (2) ◽  
pp. 557
Author(s):  
M. Sudha
Keyword(s):  
Fuzzy Logic ◽  
Distribution System ◽  
Discrete Wavelet ◽  
Current Waveform ◽  
Mother Wavelet ◽  
Fuzzy Logic Algorithm ◽  
Fault Type

This paper exhibits the best possible information example of fluffy rationale calculation for blame sort characterization in underground link. The proposed calculation utilizing mix of discrete wavelet changes (DWT) and fluffy rationale. The DWT is connected to concentrate high recurrence segment from blame current waveform utilizing mother wavelet daubechies4 (db4). The most extreme coefficients detail of DWT and greatest proportion of DWT, acquired from stage A, B, C and zero succession of blame current waveforms have been utilized as an info factors for choice calculation. The acquired outcomes in term of normal exactness have demonstrated that the most extreme proportion of DWT can accomplished tasteful precision in blame sort order.

scholarly journals Combined DFT and Fuzzy Based Faulty Phase Selection and Classification in a Series Compensated Transmission Line

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Praveen Kumar Mishra ◽  
Anamika Yadav
Keyword(s):  
Transmission Line ◽  
Fault Location ◽  
Fault Classification ◽  
Testing Time ◽  
Heavy Load ◽  
Phase Selection ◽  
Protection Scheme ◽  
Fault Resistance ◽  
Zero Sequence ◽  
Series Capacitor

The conventional distance protection scheme malfunctions sometimes in case of a fixed series capacitor compensated transmission line due to the change in relaying impedance of the protected line during faulty conditions. In order to mitigate this problem, a combined discrete Fourier transform and fuzzy (CDFTF) based algorithm has been proposed in this paper. This method has been tested on a 400 km, 735 kV series compensated transmission line network and WSCC 3-machine 9-bus system for all fault types using MATLAB/Simulink and PSCAD platforms, respectively. A fixed series capacitor is located at the middle of the protected line. The fundamental components of phase currents, phase voltages, and zero-sequence current are fed as inputs to the proposed scheme. The fault detection, faulty phase selection, and fault classification are achieved within 1/2–1 cycle of power frequency. The proposed CDFTF-based scheme is less complex and is better than other data mining techniques which require huge training and testing time. Test results corroborate the proposed scheme reliability with wide variations in fault location, fault resistance, fault inception angle, evolving faults, compensation level, and heavy load interconnection. The results discussed in this work indicate that the proposed technique is resilient to wide variations in fault and system conditions.

MOBILE ROBOT MOTOR BEARING FAULT DETECTION AND CLASSIFICATION ON DISCRETE WAVELET TRANSFORM AND LSTM NETWORK

2018 ◽  
Vol 18 (08) ◽  
pp. 1840034 ◽  
Author(s):  
SHIWEI LI ◽  
YONGPING ZHAO ◽  
MINGLI DING
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Mobile Robot ◽  
Discrete Wavelet Transform ◽  
Vibration Signal ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Bearing Fault ◽  
Fault Type ◽  
Motor Bearing

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed.

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