scholarly journals Transient Fault Area Location and Fault Classification for Distribution Systems Based on Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (ANFIS)

2018 ◽  
Vol 16 (2) ◽  
Author(s):  
Ali Khaleghi ◽  
Mahmood Oukati Sadegh ◽  
Mahdi Ghazizadeh-Ahsaee ◽  
Alireza Mehdipour Rabori
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Inference System ◽  
Distribution Systems ◽  
Fuzzy Inference ◽  
Fault Classification ◽  
Transient Fault ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

scholarly journals Damage Detection of Irregular Plates and Regular Dams by Wavelet Transform Combined Adoptive Neuro Fuzzy Inference System

2018 ◽  
Vol 4 (2) ◽  
pp. 305 ◽  
Author(s):  
D. Hamidian ◽  
J. Salajegheh ◽  
E. Salajegheh
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Damage Zone ◽  
Detection Methods ◽  
Mode Shapes ◽  
Inference System ◽  
Neuro Fuzzy

This paper presents a technique for irregular plate and regular dam damage detection based on combination of wavelet with adoptive neuro fuzzy inference system (ANFIS). Many damage detection methods need response of structures (such as the displacements, stresses or mode shapes) before and after damage, but this method only requires response of structures after damage, otherwise many damage detection methods study regular plate but this method also studies irregular plate. First, the structure (irregular plate or regular dam) is modelled by using ANSYS software, the model is analysed and structure’s responses with damage are obtained by finite element approach. Second, the responses at the finite element points with regular distances are obtained by using ANFIS. The damage zone is represented as the elements with reduced elasticity modules. Then these responses of structures are analysed with 2D wavelet transform. It is shown that matrix detail coefficients of 2D wavelet transform can specified the damage zone of plates and regular dams by perturbation in the damaged area.

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