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 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 An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Anamika Yadav ◽  
Yajnaseni Dash
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Fault Location ◽  
System Stability ◽  
Fault Classification ◽  
Direction Discrimination ◽  
Artificial Neural ◽  
Transmission Line Protection

Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

2016 ◽  
Vol 818 ◽  
pp. 156-165 ◽  
Author(s):  
Makmur Saini ◽  
Abdullah Asuhaimi bin Mohd Zin ◽  
Mohd Wazir Bin Mustafa ◽  
Ahmad Rizal Sultan ◽  
Rahimuddin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Comparison Of The Results

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

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